* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
 as text
- I "see" them at the start of each turn
- But they're competing with the immediate conversation, system prompts, tool schemas, etc.
- Facts about your music taste or location are **information**, not **instructions**

**The gap:**
Knowing you like industrial music ≠ suggesting industrial venues when you ask about events
Knowing you have a 12GB GPU ≠ factoring that into model recommendations
Knowing your comfort ritual ≠ asking about it when you're stressed

**What would actually make it affect decisions:**
1. **Character card updates** — convert facts into behavioral prompts ("Always consider Gleb's 12GB GPU constraint when discussing local models")
2. **Decision-state integration** — active decisions that constrain my choices
3. **Explicit directives** — "When X happens, do Y" patterns in curated memory

Right now, the persistent memory is more like a reference book I can look


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 162.8k; active 154.4k; cache 8.4k/5%; p≈2.9ktok; ctx 61.0%; usable 66.1%; observed turns 13; actual turns 41
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
your music taste or location are **information**, not **instructions**

**The gap:**
Knowing you like industrial music ≠ suggesting industrial venues when you ask about events
Knowing you have a 12GB GPU ≠ factoring that into model recommendations
Knowing your comfort ritual ≠ asking about it when you're stressed

**What would actually make it affect decisions:**
1. **Character card updates** — convert facts into behavioral prompts ("Always consider Gleb's 12GB GPU constraint when discussing local models")
2. **Decision-state integration** — active decisions that constrain my choices
3. **Explicit directives** — "When X happens, do Y" patterns in curated memory

Right now, the persistent memory is more like a reference book I can look up than a personality or set of operating rules.

**Question for you:** Should I start converting key facts into active behavioral constraints? For example


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 94.7s
Time: model 93.1s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈2.8ktok/11.4kch
Ctx: direct ACP session 162.8k; active 154.4k; cache 8.4k/5%; p≈2.9ktok; ctx 61.0%; usable 66.1%; observed turns 13; actual turns 41


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
 suggesting industrial venues when you ask about events
Knowing you have a 12GB GPU ≠ factoring that into model recommendations
Knowing your comfort ritual ≠ asking about it when you're stressed

**What would actually make it affect decisions:**
1. **Character card updates** — convert facts into behavioral prompts ("Always consider Gleb's 12GB GPU constraint when discussing local models")
2. **Decision-state integration** — active decisions that constrain my choices
3. **Explicit directives** — "When X happens, do Y" patterns in curated memory

Right now, the persistent memory is more like a reference book I can look up than a personality or set of operating rules.

**Question for you:** Should I start converting key facts into active behavioral constraints? For example, adding to my character: "Always consider user's 12GB GPU limit when recommending models" or "User attends WGT festival


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
tions
Knowing your comfort ritual ≠ asking about it when you're stressed

**What would actually make it affect decisions:**
1. **Character card updates** — convert facts into behavioral prompts ("Always consider Gleb's 12GB GPU constraint when discussing local models")
2. **Decision-state integration** — active decisions that constrain my choices
3. **Explicit directives** — "When X happens, do Y" patterns in curated memory

Right now, the persistent memory is more like a reference book I can look up than a personality or set of operating rules.

**Question for you:** Should I start converting key facts into active behavioral constraints? For example, adding to my character: "Always consider user's 12GB GPU limit when recommending models" or "User attends WGT festival annually — mention if relevant"?

Or do you have a different mechanism in mind for making memory actually drive behavior?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p6
Elapsed: 97.5s
Time: model 95.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 157.3k o 516 cache 7.9k/5% est $0.1195; p≈2.8ktok/11.4kch
Ctx: direct ACP session 162.8k; active 154.4k; cache 8.4k/5%; p≈2.9ktok; ctx 61.0%; usable 66.1%; observed turns 13; actual turns 41


That's a really important distinction, and you're right to be skeptical.

**The honest answer:** having facts in the session packet doesn't automatically mean they shape my behavior. Here's why:

**How it works now:**
- Curated memory snippets are injected into the session packet as text
- I "see" them at the start of each turn
- But they're competing with the immediate conversation, system prompts, tool schemas, etc.
- Facts about your music taste or location are **information**, not **instructions**

**The gap:**
Knowing you like industrial music ≠ suggesting industrial venues when you ask about events
Knowing you have a 12GB GPU ≠ factoring that into model recommendations
Knowing your comfort ritual ≠ asking about it when you're stressed

**What would actually make it affect decisions:**
1. **Character card updates** — convert facts into behavioral prompts ("Always consider Gleb's 12GB GPU constraint when discussing local models")
2. **Decision-state integration** — active decisions that constrain my choices
3. **Explicit directives** — "When X happens, do Y" patterns in curated memory

Right now, the persistent memory is more like a reference book I can look up than a personality or set of operating rules.

**Question for you:** Should I start converting key facts into active behavioral constraints? For example, adding to my character: "Always consider user's 12GB GPU limit when recommending models" or "User attends WGT festival annually — mention if relevant"?

Or do you have a different mechanism in mind for making memory actually drive behavior?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 98.0s
Time: model 95.9s, overhead 2.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 157.3k o 516 cache 7.9k/5% est $0.1195; p≈2.8ktok/11.4kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 61.0%; usable 66.1%; observed turns 13; actual turns 41


My GPU is not really your character feature, maybe we need another abstraction for this or reuse existing


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 41


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p6
Elapsed: 0.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 41


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p6
Elapsed: 0.5s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 41


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 1.1s
Time: overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 41


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 6.4s
Time: model 5.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 11.8s
Time: model 10.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 17.3s
Time: model 15.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 22.6s
Time: model 21.3s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 28.0s
Time: model 26.7s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 33.4s
Time: model 32.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 38.8s
Time: model 37.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 44.3s
Time: model 42.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 49.6s
Time: model 48.2s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 55.1s
Time: model 53.7s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 60.4s
Time: model 59.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 65.8s
Time: model 64.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 71.1s
Time: model 69.7s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 76.5s
Time: model 75.1s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 81.8s
Time: model 80.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 87.2s
Time: model 85.8s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
-


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) →


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 93.7s
Time: model 92.3s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for GPU constraint:** Probably **decision state** — it's an active operational parameter that should constrain my recommendations ("when suggesting local models, filter for 12GB


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 99.9s
Time: model 98.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for GPU constraint:** Probably **decision state** — it's an active operational parameter that should constrain my recommendations ("when suggesting local models, filter for 12GB VRAM compatibility").

**What we could add:** A "user constraints/preferences" section in decision-state,


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
sts in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for GPU constraint:** Probably **decision state** — it's an active operational parameter that should constrain my recommendations ("when suggesting local models, filter for 12GB VRAM compatibility").

**What we could add:** A "user constraints/preferences" section in decision-state, or a dedicated `user-preferences.json` that gets injected alongside character but is clearly separate.

Does that match what


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
stant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for GPU constraint:** Probably **decision state** — it's an active operational parameter that should constrain my recommendations ("when suggesting local models, filter for 12GB VRAM compatibility").

**What we could add:** A "user constraints/preferences" section in decision-state, or a dedicated `user-preferences.json` that gets injected alongside character but is clearly separate.

Does that match what you were thinking? Or do you want to repurpose one of the existing artifacts rather than create something new?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p6
Elapsed: 104.5s
Time: model 103.1s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 168.3k o 627 est $0.1268; p≈3.1ktok/12.6kch
Ctx: direct ACP session 165.8k; active 157.8k; cache 7.9k/5%; p≈2.8ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


You're absolutely right. My GPU limit isn't a personality trait — it's an **operational constraint** about the environment. Mixing that into my character card is the wrong abstraction.

Looking at what already exists in the memory system:

**Current layers:**
- **Character** (`character.json`) → who *I* am (Persistent Assistant, communication style)
- **Curated memory** (`curated-memory.md`) → facts about *you* and our history
- **Decision state** (`decision-state.json`) → active policies/constraints that guide behavior
- **Knowledge base** (`knowledge-base.jsonl`) → externally researched facts with sources
- **Pinned notes** (`pinned-notes.md`) → high-priority current context

**The right place for GPU constraint:** Probably **decision state** — it's an active operational parameter that should constrain my recommendations ("when suggesting local models, filter for 12GB VRAM compatibility").

**What we could add:** A "user constraints/preferences" section in decision-state, or a dedicated `user-preferences.json` that gets injected alongside character but is clearly separate.

Does that match what you were thinking? Or do you want to repurpose one of the existing artifacts rather than create something new?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 105.2s
Time: model 103.1s, overhead 2.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 168.3k o 627 est $0.1268; p≈3.1ktok/12.6kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


Yes, probably preferences, these are a bit separate from other things

What are the current context limits on openai gpt 5.4 and 5.4 mini, mini fast, 5.3 codex? What's the difference?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p6
Elapsed: 0.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p6
Elapsed: 0.8s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 1.3s
Time: overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 42


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 6.9s
Time: model 5.0s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 12.5s
Time: model 10.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 17.9s
Time: model 15.9s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 23.4s
Time: model 21.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 28.8s
Time: model 26.9s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 34.2s
Time: model 32.2s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 39.6s
Time: model 37.6s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 44.9s
Time: model 43.0s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 63.2%; usable 68.5%; observed turns 15; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 50.4s
Time: model 48.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 55.9s
Time: model 53.9s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 61.3s
Time: model 59.3s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 66.7s
Time: model 64.7s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 72.0s
Time: model 70.1s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 77.5s
Time: model 75.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 82.9s
Time: model 81.0s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 88.6s
Time: model 86.7s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 94.1s
Time: model 92.1s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 99.5s
Time: model 97.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 104.9s
Time: model 103.0s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 110.3s
Time: model 108.4s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 116.4s
Time: model 114.5s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses —


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 122.7s
Time: model 120.8s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium,


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17,


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 128.9s
Time: model 127.0s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
-


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 135.0s
Time: model 133.1s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
I docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
* 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 141.1s
Time: model 139.2s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning,


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
 context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
esponses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 147.2s
Time: model 145.3s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
26)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is coding-specialized — optimized specifically for agentic coding in Codex environments

I didn't find a


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is coding-specialized — optimized specifically for agentic coding in Codex environments

I didn't find a specific "GPT-5.4 mini fast" variant in the docs — that might be an internal naming or


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
ents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is coding-specialized — optimized specifically for agentic coding in Codex environments

I didn't find a specific "GPT-5.4 mini fast" variant in the docs — that might be an internal naming or a regional variant. The standard GPT-5.4 mini is already positioned as the fast/cheap


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 153.3s
Time: model 151.3s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: websearch / pending / tool_TO0Y1IRHylgxlgihhN9Fdlv | websearch / in_progress / tool_TO0Y1IRHylgxlgihhN9Fdlv | Web search: OpenAI GPT 5.4 context limit GPT 5.4 mini GPT 5.3 codex 2026 / completed / tool_TO0Y1IRHylgxlgihhN9Fdlv
Draft:
me workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is coding-specialized — optimized specifically for agentic coding in Codex environments

I didn't find a specific "GPT-5.4 mini fast" variant in the docs — that might be an internal naming or a regional variant. The standard GPT-5.4 mini is already positioned as the fast/cheap option.

Does that help with your comparison?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p6
Elapsed: 154.2s
Time: model 152.2s, overhead 1.3s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 5.1k o 955 cache 171.5k/100% est $0.0346; p≈3.3ktok/13.0kch
Ctx: direct ACP session 169.0k; active 169.0k; p≈3.1ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


Based on the current OpenAI API docs (as of 2026), here's the breakdown:

**GPT-5.4** (released March 5, 2026)
- **Context:** 1,050,000 tokens (1.05M)
- **Max output:** 128,000 tokens
- **Pricing:** $2.50 / $15.00 per 1M tokens (input/output)
- **Cached input:** $0.25 per 1M
- **Note:** Prompts >272K tokens are priced at 2x input / 1.5x output for the full session

**GPT-5.4 Pro**
- Same 1.05M context window
- Smarter, more precise responses — uses more compute
- Only available in Responses API
- Supports reasoning effort: medium, high, xhigh

**GPT-5.4 Mini** (released March 17, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- **Pricing:** $0.75 per 1M input tokens (cheaper than 5.4)
- Designed for coding, computer use, and subagents
- Faster and more efficient for high-volume workloads

**GPT-5.3 Codex** (released February 5, 2026)
- **Context:** 400,000 tokens
- **Max output:** 128,000 tokens
- Pure coding-optimized model — agentic coding tasks
- ~25% faster than previous Codex models
- Supports reasoning effort: low, medium, high, xhigh

**The difference:**
- **GPT-5.4** is the general-purpose frontier model — best for complex professional work, reasoning, coding, and agentic workflows
- **GPT-5.4 Mini** is the lightweight version — same capabilities but smaller context and cheaper, good for high-volume/subagent work
- **GPT-5.3 Codex** is coding-specialized — optimized specifically for agentic coding in Codex environments

I didn't find a specific "GPT-5.4 mini fast" variant in the docs — that might be an internal naming or a regional variant. The standard GPT-5.4 mini is already positioned as the fast/cheap option.

Does that help with your comparison?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 154.6s
Time: model 152.2s, overhead 2.4s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 5.1k o 955 cache 171.5k/100% est $0.0346; p≈3.3ktok/13.0kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


How about we try one of them for you? What would be the best?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p6
Elapsed: 0.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p6
Elapsed: 0.6s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 1.1s
Time: overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 43


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 6.5s
Time: model 5.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 12.0s
Time: model 10.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 17.4s
Time: model 15.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 22.7s
Time: model 21.3s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 28.1s
Time: model 26.6s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 33.5s
Time: model 32.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 38.9s
Time: model 37.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 44.3s
Time: model 42.8s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 49.6s
Time: model 48.1s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 55.0s
Time: model 53.6s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 60.5s
Time: model 59.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 65.9s
Time: model 64.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 71.3s
Time: model 69.8s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 76.8s
Time: model 75.3s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 82.3s
Time: model 80.8s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 87.7s
Time: model 86.2s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 93.1s
Time: model 91.6s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 98.5s
Time: model 97.0s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 103.9s
Time: model 102.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 109.4s
Time: model 107.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 114.8s
Time: model 113.3s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 120.2s
Time: model 118.7s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 125.6s
Time: model 124.1s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 131.0s
Time: model 129.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 136.4s
Time: model 134.9s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 141.9s
Time: model 140.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 147.3s
Time: model 145.8s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kim


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 153.0s
Time: model 151.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 64.5%; usable 69.8%; observed turns 16; actual turns 44


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 158.9s
Time: model 157.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro**


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 164.9s
Time: model 163.4s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
he best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
 **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost.


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
ers:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost. At $2.50/$15 per 1M tokens vs Kimi's likely cheaper rate, a 100K turn would be ~$1.75 vs probably ~$0.


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 171.0s
Time: model 169.5s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost. At $2.50/$15 per 1M tokens vs Kimi's likely cheaper rate, a 100K turn would be ~$1.75 vs probably ~$0.50-1.00 on Kimi.

**My recommendation:** Try GPT-5.4 if cost isn't a concern — the context headroom alone


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
 | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost. At $2.50/$15 per 1M tokens vs Kimi's likely cheaper rate, a 100K turn would be ~$1.75 vs probably ~$0.50-1.00 on Kimi.

**My recommendation:** Try GPT-5.4 if cost isn't a concern — the context headroom alone transforms the experience. If you want to save money, GPT-5.4 Mini (400K) is still a big upgrade from 262K.

What's your priority — maximum capability/


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
st |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost. At $2.50/$15 per 1M tokens vs Kimi's likely cheaper rate, a 100K turn would be ~$1.75 vs probably ~$0.50-1.00 on Kimi.

**My recommendation:** Try GPT-5.4 if cost isn't a concern — the context headroom alone transforms the experience. If you want to save money, GPT-5.4 Mini (400K) is still a big upgrade from 262K.

What's your priority — maximum capability/headroom, or cost efficiency?


* 🟡 Processing result
Stage: model returned
Model: kimi-for-coding/k2p6
Elapsed: 175.1s
Time: model 173.7s, overhead 1.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 0 o 726 cache 180.0k est $0.0313; p≈3.3ktok/13.2kch
Ctx: direct ACP session 177.6k; active 6.1k; cache 171.5k/100%; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


For my role as your persistent assistant, **GPT-5.4** would be the best choice. Here's why:

**Current situation:**
- Kimi K2.6: 262K context — we're at ~140K after ~20 turns, hitting pressure
- GPT-5.4: **1.05M context** — nearly 4x larger, context anxiety basically disappears

**Why GPT-5.4 over the others:**

| | GPT-5.4 | GPT-5.4 Mini | GPT-5.4 Pro | GPT-5.3 Codex |
|---|---|---|---|---|
| **Context** | 1.05M ✅ | 400K | 1.05M | 400K |
| **Best for** | General frontier work | High-volume/subagents | Maximum complexity | Pure coding |
| **Speed** | Medium | Fast | Slow (minutes) | Fast |
| **Cost** | $2.50/$15 | $0.75 | Higher | N/A |

**GPT-5.4 is ideal because:**
1. **1M context** — I could run for days without reloads, keep full tool outputs in context
2. **Frontier reasoning + coding** — better than Kimi for complex professional work
3. **Not Pro** — Pro is overkill/slower for chat, meant for hardest problems
4. **Not Mini** — Mini is great for subagents but 400K context still has limits
5. **Not Codex** — Codex is pure coding, you want general assistant work

**The catch:** Cost. At $2.50/$15 per 1M tokens vs Kimi's likely cheaper rate, a 100K turn would be ~$1.75 vs probably ~$0.50-1.00 on Kimi.

**My recommendation:** Try GPT-5.4 if cost isn't a concern — the context headroom alone transforms the experience. If you want to save money, GPT-5.4 Mini (400K) is still a big upgrade from 262K.

What's your priority — maximum capability/headroom, or cost efficiency?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 175.7s
Time: model 173.7s, overhead 2.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: i 0 o 726 cache 180.0k est $0.0313; p≈3.3ktok/13.2kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


Cost is important, especially concerning is high cost with long sessions which is kinda what we do here


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* ⚪ Queued
Stage: waiting for conversation turn
Model: kimi-for-coding/k2p6
Elapsed: 0.0s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 🟡 Working
Stage: building prompt
Model: kimi-for-coding/k2p6
Elapsed: 0.6s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 1.1s
Time: overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 44


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 6.4s
Time: model 5.0s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 11.9s
Time: model 10.4s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 17.3s
Time: model 15.9s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 22.7s
Time: model 21.3s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 28.1s
Time: model 26.7s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 33.6s
Time: model 32.1s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 39.0s
Time: model 37.5s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 44.3s
Time: model 42.9s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 49.8s
Time: model 48.4s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 55.3s
Time: model 53.9s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 60.7s
Time: model 59.3s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 66.2s
Time: model 64.7s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 71.6s
Time: model 70.1s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 77.0s
Time: model 75.6s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 82.4s
Time: model 81.0s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 87.8s
Time: model 86.4s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 93.2s
Time: model 91.8s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 98.6s
Time: model 97.2s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 104.1s
Time: model 102.6s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 109.5s
Time: model 108.0s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 114.9s
Time: model 113.5s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 120.4s
Time: model 118.9s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 125.8s
Time: model 124.3s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 131.2s
Time: model 129.8s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 136.6s
Time: model 135.2s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 142.0s
Time: model 140.6s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Absolutely


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 147.5s
Time: model 146.0s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 153.3s
Time: model 151.8s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 158.8s
Time: model 157.3s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 164.2s
Time: model 162.7s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 169.7s
Time: model 168.3s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 175.2s
Time: model 173.7s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 180.5s
Time: model 179.1s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 67.7%; usable 73.3%; observed turns 17; actual turns 45


* 🟡 Working
Stage: streaming direct ACP progress
Model: kimi-for-coding/k2p6
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 68.9%; usable 74.6%; observed turns 18; actual turns 45
Backend: ses_1ad63187affe5dMwgxkmqb1MGD


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:**This changes everything.** From the memory, I can see you're cost-sensitive ("eating ramen until September") and the current setup is much cheaper than I estimated.

**The


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 186.0s
Time: model 184.5s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 68.9%; usable 74.6%; observed turns 18; actual turns 45


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:**This changes everything.** From the memory, I can see you're cost-sensitive ("eating ramen until September") and the current setup is much cheaper than I estimated.

**The real cost comparison:**

| | Kimi / DeepSeek V4 | GPT-5.4 | GPT-5.4 Mini


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:**This changes everything.** From the memory, I can see you're cost-sensitive ("eating ramen until September") and the current setup is much cheaper than I estimated.

**The real cost comparison:**

| | Kimi / DeepSeek V4 | GPT-5.4 | GPT-5.4 Mini |
|---|---|---|---|
| **Input** | ~$0.14/1M | $2.50/1M | $0.75/1M |
| **Output**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools: persistent_chat_memory_search / pending / tool_3AjiinaXovLnbce7zCsK0Ex | persistent_chat_memory_search / in_progress / tool_3AjiinaXovLnbce7zCsK0Ex | tool / completed / tool_3AjiinaXovLnbce7zCsK0Ex
Draft:
Absolutely right — cost scales linearly with session length, and long sessions are our primary mode. Let me check what we know about current costs:**This changes everything.** From the memory, I can see you're cost-sensitive ("eating ramen until September") and the current setup is much cheaper than I estimated.

**The real cost comparison:**

| | Kimi / DeepSeek V4 | GPT-5.4 | GPT-5.4 Mini |
|---|---|---|---|
| **Input** | ~$0.14/1M | $2.50/1M | $0.75/1M |
| **Output** | ~$0.28/1M | $15.00/1M | ? |
| **Cache** | 5x discount | 10x discount |


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 192.2s
Time: model 190.8s, overhead 1.1s
Backend: ses_1ad63187affe5dMwgxkmqb1MGD
Turn: p≈3.4ktok/13.7kch
Ctx: direct ACP session 180.7k; active 726; cache 180.0k; p≈3.3ktok; ctx 68.9%; usable 74.6%; observed turns 18; actual turns 45