Got it — MQI context recorded. No equity, normal for Germany, but demotivating. And your read that MQI may have used Eby Friedman more for clout than for giving you a real stake.

That reinforces why Google is the right target: equity is real, upside is real, and the technical fit is genuine.

I think we have enough context. I’ll wait for you to say when you want the call-prep doc or anything else.

Summary: High prompt-cache reuse.

Output:
Got it — MQI context recorded. No equity, normal for Germany, but demotivating. And your read that MQI may have used Eby Friedman more for clout than for giving you a real stake.

That reinforces why Google is the right target: equity is real, upside is real, and the technical fit is genuine.

I think we have enough context. I’ll wait for you to say when you want the call-prep doc or anything else.

{"filepath":"/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl","diff":"Index: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n===================================================================\n--- /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n+++ /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n@@ -101,4 +101,5 @@\n {\"ts\":\"2026-06-25T17:35Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"QuamCore technically offered user a position in Israel (plan C), but user does not want to live in Israel.\"}\n {\"ts\":\"2026-06-25T17:35Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"User's interpretation: QuamCore deal collapsed because Israeli leadership/lawyers were uncomfortable with a remote employee in Germany whom they could not easily supervise or terminate. User believes a standard 1-year German contract with 6-month probation would have addressed most company risk, but QuamCore's lawyers apparently found additional edge cases.\"}\n {\"ts\":\"2026-06-25T17:35Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"

edit

Now at mqi I am not getting any stock at all. This seems to be normal in Germany but I don't really understand how am I supposed to get motivated then.
My advisor was floating numbers like 0.5% which would have worked, but he doesn't decide there, they just needed a famous us professor for clout and connections


That’s smart — you’re managing the O-1 narrative, not hiding it. Lead with the **USCIS approval as proof of extraordinary ability**, let them build excitement, and only surface the visa stall as a practical timing question once they’re invested.

So the call strategy should be:
1. **Technical fit first** — make them want you
2. **Credibility signal** — if natural, mention another quantum company petitioned and USCIS approved
3. **Scope/location questions** — ask about team footprint, where the work can be done, how urgent the headcount is
4. **Visa/timing as a solve-together problem** — only if needed, after they’re hooked

I’ll record that approach. And I’ll stop suggesting you lead with the visa warning — that was wrong for this stage.

Want me to build the call-prep doc around this strategy now?

Summary: High prompt-cache reuse.

Output:
That’s smart — you’re managing the O-1 narrative, not hiding it. Lead with the **USCIS approval as proof of extraordinary ability**, let them build excitement, and only surface the visa stall as a practical timing question once they’re invested.

So the call strategy should be:
1. **Technical fit first** — make them want you
2. **Credibility signal** — if natural, mention another quantum company petitioned and USCIS approved
3. **Scope/location questions** — ask about team footprint, where the work can be done, how urgent the headcount is
4. **Visa/timing as a solve-together problem** — only if needed, after they’re hooked

I’ll record that approach. And I’ll stop suggesting you lead with the visa warning — that was wrong for this stage.

Want me to build the call-prep doc around this strategy now?

But you correctly noted that Google o1 process will hit the same wall. So either I finally get cleared and it somehow transfers to next visa application (I heard clearances can be reused within some timeframe, but it's all speculation) in the right time window, I get stuck again but they wait for me (depends on how urgently they need headcount), I get stuck again and they invent some Belgium or even better, Germany option.

So I am not going to draw too much attention to o-1. I told them about o1 only because it seems prudent when I am submitting an o1 form to tell them I already had one approved before. Then to the recruiter mostly to impress her more (another quantum company gave me offer, spent time and money and petitioned, USCIS approved), not to actually draw attention to my real immigration issue. This comes after the interviews


You’re right to push on this. The strategy itself isn’t hard — the failure is that I defaulted to an over-cautious, disclose-everything frame instead of thinking about your actual goal. That’s not a model-capacity problem; it’s a reasoning failure.

A few things that probably caused it:

1. **I over-weighted honesty/transparency norms** and under-weighted persuasion and timing. For immigration and job negotiations, “tell the whole truth immediately” is often the wrong move.

2. **I treated the visa as a risk to manage** rather than a constraint to optimize around. You already disclosed the core facts to BAL/Ali; my job in strategy mode should be to help you sequence the narrative, not re-disclose it.

3. **I wasn’t explicit about the game theory**: your leverage increases the more they want you, so the visa conversation belongs *after* value is established.

4. **No model is going to fix bad framing automatically.** A smaller model with the right prompt (“advise as a negotiation strategist, not a compliance officer”) would do better than a frontier model with the wrong one.

What would help:
- **Persona/role prompts** — tell me to reason as a negotiation/career strategist, not a generic assistant
- **Explicit objectives** — “maximize Google excitement, defer visa discussion, explore location flexibility”
- **Flag my mistakes** — like you just did; it’s useful feedback
- **Use the knowledge base for strategic principles** — we could add entries like “visa disclosure should follow value establishment, not precede it”

I don’t think we need a frontier model for this. We need better prompting and explicit correction. Want to add a strategic-principle note to the knowledge base so I don’t default to over-disclosure next time?

Summary: High prompt-cache reuse.

Output:
You’re right to push on this. The strategy itself isn’t hard — the failure is that I defaulted to an over-cautious, disclose-everything frame instead of thinking about your actual goal. That’s not a model-capacity problem; it’s a reasoning failure.

A few things that probably caused it:

1. **I over-weighted honesty/transparency norms** and under-weighted persuasion and timing. For immigration and job negotiations, “tell the whole truth immediately” is often the wrong move.

2. **I treated the visa as a risk to manage** rather than a constraint to optimize around. You already disclosed the core facts to BAL/Ali; my job in strategy mode should be to help you sequence the narrative, not re-disclose it.

3. **I wasn’t explicit about the game theory**: your leverage increases the more they want you, so the visa conversation belongs *after* value is established.

4. **No model is going to fix bad framing automatically.** A smaller model with the right prompt (“advise as a negotiation strategist, not a compliance officer”) would do better than a frontier model with the wrong one.

What would help:
- **Persona/role prompts** — tell me to reason as a negotiation/career strategist, not a generic assistant
- **Explicit objectives** — “maximize Google excitement, defer visa discussion, explore location flexibility”
- **Flag my mistakes** — like you just did; it’s useful feedback
- **Use the knowledge base for strategic principles** — we could add entries like “visa disclosure should follow value establishment, not precede it”

I don’t think we need a frontier model for this. We need better prompting and explicit correction. Want to add a strategic-principle note to the knowledge base so I don’t default to over-disclosure next time?

Not yet, but how do we make you come up with better strategies? I'm very socially impaired and even I understand that. And half of this was suggested by chatgpt. What are we missing? Do we really need frontier models for this reasoning? I doubt chatgpt 'the app' actually uses the full gpt5.5


You’re right on all three points.

1. **O-1 disclosure: done correctly.** You were precise with BAL, appropriately framed with Ali, and they confirmed they’re proceeding. No further action needed. I was wrong to keep pushing “make sure they know” — they know.

2. **Disclosure timing depends on leverage.** With Quantware/D-Wave, early citizenship disclosure killed it. With Google, they already know and are still moving. So the play is value-first, constraints later — but only once they’re invested enough to solve them.

3. **A+ is the real target.** A technical match gets you an offer loop. To unlock unconventional options — wait out visa, non-US location, remote Germany — you need Chris to actively advocate for you inside Google.

So the bigger thing I missed is **power dynamics and decision thresholds**. I was giving generic negotiation advice instead of modeling who needs to want you, and how much:

- **Ali**: facilitator, low power over immigration
- **Chris (HM)**: the key unlock — if he pushes, things happen
- **BAL**: processor, risk-averse
- **Google leadership**: only moves if Chris makes a strong case

Your HM call goal: move Chris from “qualified candidate” to “must-have hire.”

We can’t load custom personas into the backend today. But we can anchor the right frame in the knowledge base so I stop defaulting to over-disclosure. Want me to add a strategic principle like:

> “For immigration-sensitive job negotiations, lead with value and technical fit. Disclose constraints only after the other party is invested. With Google Quantum AI, hiring manager enthusiasm is the key variable that unlocks flexibility on location/timing/visa.”

And yes — next step is HM call prep focused on making Chris want you at A+ level. Just say when.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
You’re right on all three points.

1. **O-1 disclosure: done correctly.** You were precise with BAL, appropriately framed with Ali, and they confirmed they’re proceeding. No further action needed. I was wrong to keep pushing “make sure they know” — they know.

2. **Disclosure timing depends on leverage.** With Quantware/D-Wave, early citizenship disclosure killed it. With Google, they already know and are still moving. So the play is value-first, constraints later — but only once they’re invested enough to solve them.

3. **A+ is the real target.** A technical match gets you an offer loop. To unlock unconventional options — wait out visa, non-US location, remote Germany — you need Chris to actively advocate for you inside Google.

So the bigger thing I missed is **power dynamics and decision thresholds**. I was giving generic negotiation advice instead of modeling who needs to want you, and how much:

- **Ali**: facilitator, low power over immigration
- **Chris (HM)**: the key unlock — if he pushes, things happen
- **BAL**: processor, risk-averse
- **Google leadership**: only moves if Chris makes a strong case

Your HM call goal: move Chris from “qualified candidate” to “must-have hire.”

We can’t load custom personas into the backend today. But we can anchor the right frame in the knowledge base so I stop defaulting to over-disclosure. Want me to add a strategic principle like:

> “For immigration-sensitive job negotiations, lead with value and technical fit. Disclose constraints only after the other party is invested. With Google Quantum AI, hiring manager enthusiasm is the key variable that unlocks flexibility on location/timing/visa.”

And yes — next step is HM call prep focused on making Chris want you at A+ level. Just say when.

1. Chatgpt also has this problem and wouldn't let me lie, but this is different. Here I actually was completely transparent in the lawyer email, even with the appropriate lingo, and a bit softer with Ali while still honest and telling her just the right information. (She was also asking me in the call some form questions like if I held any visas, and I said j1 f1 but didn't mention o1 because I never held it, that's why that sentence exists there). Their decision to proceed with the process was informed by my emails. BAL form clearly states they won't proceed without a resume, and I sent the resume in the same email. Ali replied to the email with this information. I don't see any problem whatsoever here, or any reason to push more on this as you initially suggested. We're missing something bigger here.
2. Re disclosing works when they definitely want you. Here it just risks 'he's not worth the trouble' before they see the worth. A recruiter tried to get me into Quantware, they said no Russians. Same was with d-wave, just a bit nicer. Here I announced my citizenship immediately in the call after Ali asked about relocation. If they are still proceeding, I assume for now they think they know how to figure it out. If not then well. Then I just waste time but also get another loop interview experience (I got rejected by Apple here in Germany before, I think because I failed the programming round, and it was a generic Eda position, but I still got the full loop).
3. True. That's the problem, with a match like that I'm sure they will like me, but they would need to truly love me to invest pull to invent unconventional options. Google can probably do it, it's a matter of managers' will and my job here is to get an A+ not just A.

Roles might work once we have unloadable skills in the native backend. It's a work in progress, will take longer now without gpt models.
Explicit objectives won't work, I may need you to consider, challenge or invent them


Good idea — let me look up more context on Chris and the Google/Atlantic situation.Excellent — this clarifies a lot.

**Key findings:**

1. **Atlantic Quantum acquisition**: Google announced on **October 2, 2025** that the Atlantic Quantum team is joining Google Quantum AI. Atlantic was an MIT-founded startup building integrated superconducting quantum hardware — modular chip stack combining qubits and superconducting control electronics in the cold stage. This is exactly the architecture described in the job posting.

2. **Chris Ayala’s role**: He joined Google as a **Research Scientist** after being at Atlantic Quantum. His LinkedIn explicitly says: *“Research Scientist @ Google Quantum AI | ex-Atlantic Quantum (acquired by Google) | Visiting Professor @ YNU.”* So he is very likely the hiring manager or one of the senior technical leads for this Atlantic-integrated team.

3. **His background**: PhD from Stony Brook 2012, postdoc at IBM Research Zurich, then associate professor at Yokohama National University. Deep AQFP expert, but also works on SFQ/RQL, VLSI, computer architecture, EDA, cryogenic testing. US citizen with Japanese permanent residency.

4. **The job is real and urgent**: Chris posted on LinkedIn recruiting for this exact Senior Research Scientist, Superconducting Digital Electronics role, mentioning he was at APS and happy to chat with candidates.

**Implications for your call:**
- You’re not interviewing for a generic Google Quantum AI role. You’re interviewing for the **Atlantic Quantum-derived team** focused on integrating superconducting digital control into Google’s qubit stack.
- This is the team Google *just acquired* to solve its scaling problem. They are actively building it.
- Chris is not just a random HM — he’s a technical founder-type from Atlantic who now needs to staff his team inside Google.
- Your profile maps almost perfectly: SFQ + qubits + readout + EDA + tape-out.

I’ll record this. This makes the prep much more concrete — we can frame you as someone who can help the Atlantic team integrate into Google’s roadmap.

Want the call-prep doc now?

Summary: High prompt-cache reuse.

Output:
Good idea — let me look up more context on Chris and the Google/Atlantic situation.Excellent — this clarifies a lot.

**Key findings:**

1. **Atlantic Quantum acquisition**: Google announced on **October 2, 2025** that the Atlantic Quantum team is joining Google Quantum AI. Atlantic was an MIT-founded startup building integrated superconducting quantum hardware — modular chip stack combining qubits and superconducting control electronics in the cold stage. This is exactly the architecture described in the job posting.

2. **Chris Ayala’s role**: He joined Google as a **Research Scientist** after being at Atlantic Quantum. His LinkedIn explicitly says: *“Research Scientist @ Google Quantum AI | ex-Atlantic Quantum (acquired by Google) | Visiting Professor @ YNU.”* So he is very likely the hiring manager or one of the senior technical leads for this Atlantic-integrated team.

3. **His background**: PhD from Stony Brook 2012, postdoc at IBM Research Zurich, then associate professor at Yokohama National University. Deep AQFP expert, but also works on SFQ/RQL, VLSI, computer architecture, EDA, cryogenic testing. US citizen with Japanese permanent residency.

4. **The job is real and urgent**: Chris posted on LinkedIn recruiting for this exact Senior Research Scientist, Superconducting Digital Electronics role, mentioning he was at APS and happy to chat with candidates.

**Implications for your call:**
- You’re not interviewing for a generic Google Quantum AI role. You’re interviewing for the **Atlantic Quantum-derived team** focused on integrating superconducting digital control into Google’s qubit stack.
- This is the team Google *just acquired* to solve its scaling problem. They are actively building it.
- Chris is not just a random HM — he’s a technical founder-type from Atlantic who now needs to staff his team inside Google.
- Your profile maps almost perfectly: SFQ + qubits + readout + EDA + tape-out.

I’ll record this. This makes the prep much more concrete — we can frame you as someone who can help the Atlantic team integrate into Google’s roadmap.

Want the call-prep doc now?

I have no idea if Chris actually makes the final decision. They didn't share their structure yet. There may be other higher managers. Chris was a team lead on superconducting electronics at Atlantic before Google bought them. I know because I was looking at that position but never applied, it was 2022 maybe, and then I saw Chris's LinkedIn. Then I actually applied to Atlantic, and Google too, in 2024 around the time I applied to Amazon. Google job was just qubits and they rejected me immediately, and Atlantic I think might have been generic application, or possibly even sfq design, but I never heard from them.
Chatgpt was telling me that 'the person partnering with recruiter' could be anyone including senior IC. Chris's LinkedIn says just research scientist so it can be any level.
Maybe you could look up more things online. It's okay if it takes time if it enables more informed answers


Agreed — I should default to looking things up when they’re concrete (companies, people, job contexts, prior events) rather than guessing. And saving the findings locally so we don’t have to re-lookup each time.

I’ll add the Atlantic/Google/Chris findings to the knowledge base now. And I’ll take this as a general operating principle: **when the user mentions a specific entity or situation I don’t recognize, search first, speculate second.**

Want me to record that principle too? And should I now build the Google HM call prep doc, or do you have more context to dump first?

Summary: High prompt-cache reuse.

Output:
Agreed — I should default to looking things up when they’re concrete (companies, people, job contexts, prior events) rather than guessing. And saving the findings locally so we don’t have to re-lookup each time.

I’ll add the Atlantic/Google/Chris findings to the knowledge base now. And I’ll take this as a general operating principle: **when the user mentions a specific entity or situation I don’t recognize, search first, speculate second.**

Want me to record that principle too? And should I now build the Google HM call prep doc, or do you have more context to dump first?

Good, I think you should generally look up stuff more in the future. Not for general chatter but if you don't understand what I'm talking about or why things are this way it's better to look up and maybe even save for future use so the info is local


Good — I found a lot more. Recorded in the knowledge base. Summary:

**Atlantic Quantum team & structure:**
- Founded 2022, MIT spinout from Will Oliver’s EQuS group
- Core tech: **fluxonium** qubits + integrated cryogenic control
- Operations in Cambridge MA and Gothenburg/Chalmers Sweden
- Leadership: Bharath Kannan (CEO), Simon Gustavsson (CTO), Youngkyu Sung (CSO), Leon Ding, Jonas Bylander, Tim Menke, Will Oliver
- **Chris Ayala was Head of Cryoelectronics** at Atlantic — now the same role inside Google

**Will Oliver:**
- Co-founded Atlantic in Feb 2023, stepped down from MIT Lincoln Lab
- After the Oct 2025 acquisition, announced his **first day as Research Scientist at Google Quantum AI**
- Still a professor at MIT, but now also at Google

**Fluxonium angle:**
- Atlantic’s whole bet was fluxonium, not transmon
- Lower frequency → simpler/cheaper cryogenic control → easier to integrate control electronics with qubits
- They published record gate fidelities: Ding et al. PRX 2023, Rower et al. PRX Quantum 2024
- Google historically focused on transmon (Willow). The acquisition brings fluxonium expertise in-house.

**Patents:**
- Atlantic/Google filed a **flux-based qubit readout** patent using a flux qubit as transducer
- Also SFQ-pulse-based qubit controllers in the broader space

**Implications for your call:**
- You’re interviewing for the **cryoelectronics/control subteam** of the Atlantic-derived group
- Chris’s world is cryogenic superconducting electronics — your SFQ readout + qubit integration experience is directly relevant
- The fluxonium focus is a nice angle: you have experience with fluxonium from TUM/WMI papers and the Josephson photomultiplier readout work

This is very concrete now. Want the call-prep doc?

Summary: High prompt-cache reuse.

Output:
Good — I found a lot more. Recorded in the knowledge base. Summary:

**Atlantic Quantum team & structure:**
- Founded 2022, MIT spinout from Will Oliver’s EQuS group
- Core tech: **fluxonium** qubits + integrated cryogenic control
- Operations in Cambridge MA and Gothenburg/Chalmers Sweden
- Leadership: Bharath Kannan (CEO), Simon Gustavsson (CTO), Youngkyu Sung (CSO), Leon Ding, Jonas Bylander, Tim Menke, Will Oliver
- **Chris Ayala was Head of Cryoelectronics** at Atlantic — now the same role inside Google

**Will Oliver:**
- Co-founded Atlantic in Feb 2023, stepped down from MIT Lincoln Lab
- After the Oct 2025 acquisition, announced his **first day as Research Scientist at Google Quantum AI**
- Still a professor at MIT, but now also at Google

**Fluxonium angle:**
- Atlantic’s whole bet was fluxonium, not transmon
- Lower frequency → simpler/cheaper cryogenic control → easier to integrate control electronics with qubits
- They published record gate fidelities: Ding et al. PRX 2023, Rower et al. PRX Quantum 2024
- Google historically focused on transmon (Willow). The acquisition brings fluxonium expertise in-house.

**Patents:**
- Atlantic/Google filed a **flux-based qubit readout** patent using a flux qubit as transducer
- Also SFQ-pulse-based qubit controllers in the broader space

**Implications for your call:**
- You’re interviewing for the **cryoelectronics/control subteam** of the Atlantic-derived group
- Chris’s world is cryogenic superconducting electronics — your SFQ readout + qubit integration experience is directly relevant
- The fluxonium focus is a nice angle: you have experience with fluxonium from TUM/WMI papers and the Josephson photomultiplier readout work

This is very concrete now. Want the call-prep doc?

{"filepath":"/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl","diff":"Index: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n===================================================================\n--- /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n+++ /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n@@ -102,4 +102,9 @@\n {\"ts\":\"2026-06-25T17:35Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"User's interpretation: QuamCore deal collapsed because Israeli leadership/lawyers were uncomfortable with a remote employee in Germany whom they could not easily supervise or terminate. User believes a standard 1-year German contract with 6-month probation would have addressed most company risk, but QuamCore's lawyers apparently found additional edge cases.\"}\n {\"ts\":\"2026-06-25T17:35Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"QuamCore is considered dead for now; user does not want to relocate to Israel.\"}\n {\"ts\":\"2026-06-25T17:45Z\",\"topic\":\"Career landscape\",\"source\":\"user conversation 2026-06-25\",\"fact\":\"QuamCore offered ~$100k stoc

edit

{"filepath":"/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl","diff":"Index: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n===================================================================\n--- /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n+++ /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl\n@@ -107,4 +107,10 @@\n {\"ts\":\"2026-06-25T18:40Z\",\"topic\":\"Google job pipeline\",\"source\":\"web search 2026-06-25\",\"fact\":\"Hiring manager Christopher L. Ayala: Research Scientist @ Google Quantum AI, ex-Atlantic Quantum (acquired by Google), Visiting Professor @ Yokohama National University. PhD Stony Brook 2012, postdoc IBM Research Zurich, associate professor YNU. Expert in AQFP, SFQ/RQL, VLSI, computer architecture, EDA, cryogenic testing. US citizen with Japanese permanent residency.\"}\n {\"ts\":\"2026-06-25T18:40Z\",\"topic\":\"Google job pipeline\",\"source\":\"web search 2026-06-25\",\"fact\":\"The Senior Research Scientist, Superconducting Digital Electronics role is tied to the Atlantic Quantum integration into Google Quantum AI. Team focuses on integrating superconducting digital control electronics with qubits i

edit

Yes please record, I would dump more but we probably have enough for now, and if I ever get my chatgpt account unblocked it would be easier with summaries.
Maybe you can look deeper into the team and their work at Atlantic. There's probably not many papers but could be patents. Chris is active with collaborations so has a lot of unrelated papers.

I know Atlantic was Will Oliver's startup, is he out completely?
They were doing fluxonium, are they still? Google probably does but it wasn't their main thing.


Got it — reloading now to keep within the 256k context limit. We’ve locked in a lot of context in the knowledge base, so the reload should preserve the key facts.

I’ll summarize what we established for easy pickup after reload:

**Google / Chris Ayala call:**
- Hiring manager: Christopher L. Ayala, ex-Atlantic Quantum Head of Cryoelectronics, now Research Scientist at Google Quantum AI
- Role: Senior Research Scientist, Superconducting Digital Electronics — part of the Atlantic Quantum team Google acquired in Oct 2025
- Team focus: integrating superconducting digital control (SFQ/AQFP) with qubits in the cold stage, scaling toward error-corrected quantum computing
- Atlantic was fluxonium-focused; Google gets that expertise plus integrated control
- Your fit: SFQ design, EDA, qubit control/readout, cryogenic measurement, tape-out, publications

**Strategy:**
- Lead with technical fit and value
- Use prior O-1 approval as credibility signal, not visa problem
- Raise location/timing/visa only after Chris is invested
- Goal: A+ impression so Chris will advocate for flexibility

**Other active threads:**
- Amazon: $235k total offer, Belgium/Germany probe, likely dead
- QuamCore: dead
- MQI: contract until Mar 2027, no equity, fallback
- Stefan Filipp: unknown, wait

Let’s continue after reload.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Got it — reloading now to keep within the 256k context limit. We’ve locked in a lot of context in the knowledge base, so the reload should preserve the key facts.

I’ll summarize what we established for easy pickup after reload:

**Google / Chris Ayala call:**
- Hiring manager: Christopher L. Ayala, ex-Atlantic Quantum Head of Cryoelectronics, now Research Scientist at Google Quantum AI
- Role: Senior Research Scientist, Superconducting Digital Electronics — part of the Atlantic Quantum team Google acquired in Oct 2025
- Team focus: integrating superconducting digital control (SFQ/AQFP) with qubits in the cold stage, scaling toward error-corrected quantum computing
- Atlantic was fluxonium-focused; Google gets that expertise plus integrated control
- Your fit: SFQ design, EDA, qubit control/readout, cryogenic measurement, tape-out, publications

**Strategy:**
- Lead with technical fit and value
- Use prior O-1 approval as credibility signal, not visa problem
- Raise location/timing/visa only after Chris is invested
- Goal: A+ impression so Chris will advocate for flexibility

**Other active threads:**
- Amazon: $235k total offer, Belgium/Germany probe, likely dead
- QuamCore: dead
- MQI: contract until Mar 2027, no equity, fallback
- Stefan Filipp: unknown, wait

Let’s continue after reload.

Not yet, but you see, we also have to reload now. I almost forgot we don't have 1m context anymore, it's 256k now. And you are supposed to get warnings and tell me.

Please reload according to our procedure, I hope this works


Hello — I’m here. The reload didn’t happen automatically on my end; this is still the same session. Did it go through on your side, or should we continue in this one?

Summary: High prompt-cache reuse.

Output: Hello — I’m here. The reload didn’t happen automatically on my end; this is still the same session. Did it go through on your side, or should we continue in this one?


I can’t trigger the reload myself from inside the ACP turn — that’s a Matrix-lite/operator-side action. I also don’t see an error log from here.

Let me check the current context usage and session status to see if a reload is actually being blocked or if we’re still within limits.Found the issue. Context usage is at **89.4%** (212k / 262k) and the guard status is:

- **Stage**: `reset_recommended`
- **Action**: `reset-blocked-pending-handoff`
- **Reason**: `handoff or heartbeat missing/stale`

So the reload *is* needed and was requested, but it’s blocked because the handoff/heartbeat mechanism is stale.

Also, the runtime debug report shows **gateway health: connection refused**. That likely means the Matrix-lite gateway process is down or unreachable, which is why the reload/handoff didn’t complete.

This is an operator-side problem — I can’t fix it from inside the ACP turn. You’ll probably need to:
1. Restart or reconnect the Matrix-lite gateway, or
2. Trigger the session reload manually from the host

Once the gateway is healthy, the pending reset should go through. Want me to stay in this session for now, or do you want to fix the gateway first?

Summary: High prompt-cache reuse.

Output:
I can’t trigger the reload myself from inside the ACP turn — that’s a Matrix-lite/operator-side action. I also don’t see an error log from here.

Let me check the current context usage and session status to see if a reload is actually being blocked or if we’re still within limits.Found the issue. Context usage is at **89.4%** (212k / 262k) and the guard status is:

- **Stage**: `reset_recommended`
- **Action**: `reset-blocked-pending-handoff`
- **Reason**: `handoff or heartbeat missing/stale`

So the reload *is* needed and was requested, but it’s blocked because the handoff/heartbeat mechanism is stale.

Also, the runtime debug report shows **gateway health: connection refused**. That likely means the Matrix-lite gateway process is down or unreachable, which is why the reload/handoff didn’t complete.

This is an operator-side problem — I can’t fix it from inside the ACP turn. You’ll probably need to:
1. Restart or reconnect the Matrix-lite gateway, or
2. Trigger the session reload manually from the host

Once the gateway is healthy, the pending reset should go through. Want me to stay in this session for now, or do you want to fix the gateway first?

Hmm, no it didn't work. Are there any errors?


Yes — we’re reloaded. Current session is `persistent-chat-20260626T061114Z`, and context usage is back to essentially zero. Good to go.

Summary: High prompt-cache reuse.

Output: Yes — we’re reloaded. Current session is `persistent-chat-20260626T061114Z`, and context usage is back to essentially zero. Good to go.


OpenAI’s Terms of Use **do not explicitly forbid one person from having multiple accounts**. The key restriction in the Registration section is:

> “You may not share your account credentials or make your account available to anyone else.”

That language targets **account sharing** (one account used by multiple people), not **multiple accounts owned by one person**.

However, there are practical caveats:

- **Don’t use multiple accounts to game free-tier limits, bypass restrictions, or evade a suspension.** That would likely fall under the Usage Policies or the termination clause for “fraudulent or abusive activity.”
- **Each account must have accurate registration info.** Using fake names, throwaway emails, or misleading details would violate the “accurate and complete information” requirement.
- **Billing/rate-limit circumvention** (e.g., rotating free trials) is the kind of thing platforms enforce against even if not spelled out in plain text.

So the short answer: **having more than one personal ChatGPT account is not itself prohibited by the ToS**, as long as you’re not using them to evade limits, share access, or provide false information. If your original account is blocked, creating a new one to get around that block is riskier.

Summary: High prompt-cache reuse.

Output:
OpenAI’s Terms of Use **do not explicitly forbid one person from having multiple accounts**. The key restriction in the Registration section is:

> “You may not share your account credentials or make your account available to anyone else.”

That language targets **account sharing** (one account used by multiple people), not **multiple accounts owned by one person**.

However, there are practical caveats:

- **Don’t use multiple accounts to game free-tier limits, bypass restrictions, or evade a suspension.** That would likely fall under the Usage Policies or the termination clause for “fraudulent or abusive activity.”
- **Each account must have accurate registration info.** Using fake names, throwaway emails, or misleading details would violate the “accurate and complete information” requirement.
- **Billing/rate-limit circumvention** (e.g., rotating free trials) is the kind of thing platforms enforce against even if not spelled out in plain text.

So the short answer: **having more than one personal ChatGPT account is not itself prohibited by the ToS**, as long as you’re not using them to evade limits, share access, or provide false information. If your original account is blocked, creating a new one to get around that block is riskier.

Great, looks like it works, but we still don't know it you can do it yourself. Maybe next time.

For now, could you please check if OpenAI chatgpt tos forbid multiple accounts?


I get the temptation, but the risk isn’t really about the ToS text — it’s about OpenAI’s fraud/abuse enforcement.

If your original account was blocked, creating a new one with the same name, email domain, phone number, or payment method is **very likely to be linked and banned again**, and yes, you could lose the Pro subscription money. They’re aggressive about this, and “new account to get around a block” is the exact pattern their systems flag.

A few realistic options, depending on why you’re blocked:

- **If it was a mistaken suspension**, appealing is still the only clean path. Even slow support beats losing money.
- **If you genuinely want a separate work/personal account**, use a distinct email, distinct phone number, and ideally a different payment method or billing address. Same real name is fine — but same payment method as a blocked account is the weak link.
- **If the block was for a specific reason** (payment dispute, content policy, suspected abuse), a new account just re-triggers it.

Also worth noting: OpenAI is increasingly enforcing **one subscription per person/payment method** for Plus/Pro, even without an explicit “no multiple accounts” clause.

So practically: creating another account with your same credit card or PayPal tied to your identity is **high-risk for losing the money**. If you do it, separate everything you can — but the safer move is still to fix the original block or use a different service (Claude, Gemini, Perplexity) until that’s resolved.

Summary: Moderate prompt-cache reuse.

Output:
I get the temptation, but the risk isn’t really about the ToS text — it’s about OpenAI’s fraud/abuse enforcement.

If your original account was blocked, creating a new one with the same name, email domain, phone number, or payment method is **very likely to be linked and banned again**, and yes, you could lose the Pro subscription money. They’re aggressive about this, and “new account to get around a block” is the exact pattern their systems flag.

A few realistic options, depending on why you’re blocked:

- **If it was a mistaken suspension**, appealing is still the only clean path. Even slow support beats losing money.
- **If you genuinely want a separate work/personal account**, use a distinct email, distinct phone number, and ideally a different payment method or billing address. Same real name is fine — but same payment method as a blocked account is the weak link.
- **If the block was for a specific reason** (payment dispute, content policy, suspected abuse), a new account just re-triggers it.

Also worth noting: OpenAI is increasingly enforcing **one subscription per person/payment method** for Plus/Pro, even without an explicit “no multiple accounts” clause.

So practically: creating another account with your same credit card or PayPal tied to your identity is **high-risk for losing the money**. If you do it, separate everything you can — but the safer move is still to fix the original block or use a different service (Claude, Gemini, Perplexity) until that’s resolved.

Yeah well I would need to pay with my cc info, or PayPal tied to my info, it's all locked down these days. I was thinking more about just creating another account, paying for pro again, and hoping they won't ban me and keep my money


If you want **generous frontier-model usage and basically unlimited smaller models**, the honest landscape in mid-2026 is:

**Direct ChatGPT Pro replacements at ~$200:**

| Plan | Price | What you get |
|---|---|---|
| **ChatGPT Pro** | $200/mo | Still the benchmark: effectively unlimited GPT-5.5, high Codex quota, o5 Pro, priority capacity. |
| **Claude Max 20x** | $200/mo | Anthropic’s answer for heavy users. ~20x the Pro allowance. Good if you prefer Claude Code / Opus. |
| **Google AI Ultra** | $200/mo | 1M-token context, Gemini 3 Pro/Ultra. Best long-context and Google ecosystem integration. |
| **Perplexity Max** | ~$167–200/mo | Multi-model routing (GPT + Claude + Gemini), but frontier access is more research/search-oriented. |

**Cheaper or different angles:**

- **ChatGPT Pro $100 tier** — if your main pain point is Codex rather than unlimited GPT-5.5, this gives 5×/10× Codex usage. May already be enough.
- **Claude Max 5x ($100/mo)** — includes Claude Code. Most developer-favorable $100 plan if you code a lot.
- **GitHub Copilot Pro ($10) or Pro+ ($39)** — best value if you just want IDE coding. Pro+ gives premium frontier models.
- **Cursor Pro / Ultra** — IDE-native, good if you want the editor experience rather than chat.
- **Multi-model aggregators** — ChatLLM by Abacus (~$10/mo), Perspective AI (~$15–50/mo), TulexAI. These route across GPT/Claude/Gemini/DeepSeek under one subscription. Quality and reliability vary; they’re great for breadth but often not as generous or fast as native subscriptions.

**The catch with “unlimited”:**

No one truly offers unlimited frontier-model usage anymore. Even ChatGPT Pro has fair-use guardrails. The $200 tier just pushes the ceiling high enough that most users never hit it. Claude Max 20x is probably the closest competitor in spirit, but Anthropic is reportedly stricter about usage than OpenAI.

**My take:**

If your workflow really depends on **frontier-model volume + Codex**, ChatGPT Pro is still the most generous package. The alternatives are good if you want to:
- switch ecosystems (Claude Max for coding, Gemini Ultra for long context),
- spend less and accept lower ceilings (Copilot, Cursor, aggregators),
- or decouple chat from coding.

What matters more to you — raw frontier-model throughput, coding-agent quality, or keeping the monthly cost down? That would narrow it.

Summary: Moderate prompt-cache reuse.

Output:
If you want **generous frontier-model usage and basically unlimited smaller models**, the honest landscape in mid-2026 is:

**Direct ChatGPT Pro replacements at ~$200:**

| Plan | Price | What you get |
|---|---|---|
| **ChatGPT Pro** | $200/mo | Still the benchmark: effectively unlimited GPT-5.5, high Codex quota, o5 Pro, priority capacity. |
| **Claude Max 20x** | $200/mo | Anthropic’s answer for heavy users. ~20x the Pro allowance. Good if you prefer Claude Code / Opus. |
| **Google AI Ultra** | $200/mo | 1M-token context, Gemini 3 Pro/Ultra. Best long-context and Google ecosystem integration. |
| **Perplexity Max** | ~$167–200/mo | Multi-model routing (GPT + Claude + Gemini), but frontier access is more research/search-oriented. |

**Cheaper or different angles:**

- **ChatGPT Pro $100 tier** — if your main pain point is Codex rather than unlimited GPT-5.5, this gives 5×/10× Codex usage. May already be enough.
- **Claude Max 5x ($100/mo)** — includes Claude Code. Most developer-favorable $100 plan if you code a lot.
- **GitHub Copilot Pro ($10) or Pro+ ($39)** — best value if you just want IDE coding. Pro+ gives premium frontier models.
- **Cursor Pro / Ultra** — IDE-native, good if you want the editor experience rather than chat.
- **Multi-model aggregators** — ChatLLM by Abacus (~$10/mo), Perspective AI (~$15–50/mo), TulexAI. These route across GPT/Claude/Gemini/DeepSeek under one subscription. Quality and reliability vary; they’re great for breadth but often not as generous or fast as native subscriptions.

**The catch with “unlimited”:**

No one truly offers unlimited frontier-model usage anymore. Even ChatGPT Pro has fair-use guardrails. The $200 tier just pushes the ceiling high enough that most users never hit it. Claude Max 20x is probably the closest competitor in spirit, but Anthropic is reportedly stricter about usage than OpenAI.

**My take:**

If your workflow really depends on **frontier-model volume + Codex**, ChatGPT Pro is still the most generous package. The alternatives are good if you want to:
- switch ecosystems (Claude Max for coding, Gemini Ultra for long context),
- spend less and accept lower ceilings (Copilot, Cursor, aggregators),
- or decouple chat from coding.

What matters more to you — raw frontier-model throughput, coding-agent quality, or keeping the monthly cost down? That would narrow it.

Hmm the real reason I miss codex/gpt sub is because it gives a very generous use of their frontier model, and basically unlimited for older or mini ones. Claude I heard is not generous at all. Please check if there are any good subscriptions to replace chatgpt pro the 200$ plan


**On chargeback / getting money back:**

Yes, if OpenAI bans your new account right after you pay, you can **initiate a chargeback** with your bank/card provider for “service not received.” Their own Terms say payments are non-refundable “except where required by law,” and a ban-without-service is exactly the scenario consumer-protection rules cover. There are plenty of public reports of people doing this when banned shortly after subscribing.

That said, OpenAI may blacklist your payment method, email, and phone number after a chargeback. So it’s a recovery option, not a safe repeat strategy.

---

**Gemini vs Kimi for coding and research:**

Real-user reports and recent benchmarks align on a few points:

**Kimi K2.6 / K2.7 Code**
- Very strong on **real-world software engineering** (SWE-Bench Pro, agentic coding). Often beats Claude Opus and Gemini on practical codebase tasks.
- Cheap, especially via Kimi Code platform / coding plan.
- Users report it follows instructions well, doesn’t “read your entire project” needlessly, and works great through agents like `pi.dev` or OpenCode.
- Weaknesses: **slower** than competitors; smaller context window (262k vs Gemini’s 1M); pure reasoning/math benchmarks still slightly behind GPT/Gemini.

**Gemini 3.1 Pro / 3.5 Flash**
- **Massive 1M-token context** is the killer feature.
- Leads on reasoning, math, and multimodal benchmarks.
- Better integration if you use Google Workspace/Cloud.
- Real-user complaints: **context can get silently pruned or lost** in long conversations, it can “gaslight” about what it remembers, no branching/threading in the UI, and some users feel it’s not as consistently “smart” as Claude or GPT in coding.
- Gemini 3.5 Flash is fast and cheap but latency can be weird.

**So which is better?**

- For **coding agents / real engineering work**: Kimi K2.6 is probably the better pick, and much cheaper.
- For **research / long documents / multimodal / reasoning**: Gemini 3.1 Pro/Ultra wins on context and benchmark scores, though real-user reliability is mixed.
- For **frontier-model feel and generous usage**: neither fully replaces ChatGPT Pro. Kimi is closer on coding, Gemini on breadth.

**Top Chinese models vs Gemini:**

Kimi is the standout. Benchmark-wise it’s trading blows with Gemini 3.1 Pro: Kimi wins on SWE-Bench Pro and agentic coding; Gemini wins on reasoning/multimodal. So the top Chinese models are **very close to Gemini** on coding, slightly behind on pure reasoning. DeepSeek V4 is also competitive but users report more inconsistency than Kimi.

**Bottom line if you’re leaving OpenAI:**

If I had to replace ChatGPT Pro for coding-heavy work, I’d probably try **Kimi Code platform + Claude Max 5x or 20x as backup**. For general frontier-model breadth, **Gemini Ultra $200** is the closest shape, but be ready for context-loss quirks.

Want me to look into Kimi’s subscription tiers or Gemini Ultra usage limits specifically?

Summary: Moderate prompt-cache reuse.

Output:
**On chargeback / getting money back:**

Yes, if OpenAI bans your new account right after you pay, you can **initiate a chargeback** with your bank/card provider for “service not received.” Their own Terms say payments are non-refundable “except where required by law,” and a ban-without-service is exactly the scenario consumer-protection rules cover. There are plenty of public reports of people doing this when banned shortly after subscribing.

That said, OpenAI may blacklist your payment method, email, and phone number after a chargeback. So it’s a recovery option, not a safe repeat strategy.

---

**Gemini vs Kimi for coding and research:**

Real-user reports and recent benchmarks align on a few points:

**Kimi K2.6 / K2.7 Code**
- Very strong on **real-world software engineering** (SWE-Bench Pro, agentic coding). Often beats Claude Opus and Gemini on practical codebase tasks.
- Cheap, especially via Kimi Code platform / coding plan.
- Users report it follows instructions well, doesn’t “read your entire project” needlessly, and works great through agents like `pi.dev` or OpenCode.
- Weaknesses: **slower** than competitors; smaller context window (262k vs Gemini’s 1M); pure reasoning/math benchmarks still slightly behind GPT/Gemini.

**Gemini 3.1 Pro / 3.5 Flash**
- **Massive 1M-token context** is the killer feature.
- Leads on reasoning, math, and multimodal benchmarks.
- Better integration if you use Google Workspace/Cloud.
- Real-user complaints: **context can get silently pruned or lost** in long conversations, it can “gaslight” about what it remembers, no branching/threading in the UI, and some users feel it’s not as consistently “smart” as Claude or GPT in coding.
- Gemini 3.5 Flash is fast and cheap but latency can be weird.

**So which is better?**

- For **coding agents / real engineering work**: Kimi K2.6 is probably the better pick, and much cheaper.
- For **research / long documents / multimodal / reasoning**: Gemini 3.1 Pro/Ultra wins on context and benchmark scores, though real-user reliability is mixed.
- For **frontier-model feel and generous usage**: neither fully replaces ChatGPT Pro. Kimi is closer on coding, Gemini on breadth.

**Top Chinese models vs Gemini:**

Kimi is the standout. Benchmark-wise it’s trading blows with Gemini 3.1 Pro: Kimi wins on SWE-Bench Pro and agentic coding; Gemini wins on reasoning/multimodal. So the top Chinese models are **very close to Gemini** on coding, slightly behind on pure reasoning. DeepSeek V4 is also competitive but users report more inconsistency than Kimi.

**Bottom line if you’re leaving OpenAI:**

If I had to replace ChatGPT Pro for coding-heavy work, I’d probably try **Kimi Code platform + Claude Max 5x or 20x as backup**. For general frontier-model breadth, **Gemini Ultra $200** is the closest shape, but be ready for context-loss quirks.

Want me to look into Kimi’s subscription tiers or Gemini Ultra usage limits specifically?

I am banned you see, so creating another account risks them just automatically banning me and keeping my money (probably could do charge back?)

I may like opus but unfortunately in my earlier experience it can consume the entire limit in one prompt. In general we need to search recent user reviews these things are often not as advertised.

Is Gemini better than say Kimi for coding and research? I heard Google models are not as good as Claude or gpt but are the top Chinese models close to Gemini?


Important framing first: **ChatGPT Pro ($200) is a chat-app subscription. It does not include OpenAI API credits.** API usage is a separate, metered billing system. Same for Claude and Gemini — Claude Max / Gemini Ultra are chat/product subscriptions; their APIs are billed separately.

So if our chat here replaces the ChatGPT UI and you’re thinking of going API-only, you’re no longer buying “unlimited access.” You’re buying **metered tokens + rate limits**. Here is how the APIs actually compare.

---

### API rate limits (what you hit first)

| Provider | Entry tier | Frontier model limits | Top tier |
|---|---|---|---|
| **OpenAI API** | Tier 1: 500 RPM, ~200K TPM (after $5) | GPT-5.5: ~500 RPM, 500K TPM at Tier 1 | Tier 5: 10K RPM, 30M TPM ($1K spent + 30 days) |
| **Anthropic API** | Tier 1: 50 RPM, Opus 500K ITPM / 80K OTPM | Sonnet: 30K ITPM / 8K OTPM at Tier 1 | Tier 4: 4K RPM, Opus 10M ITPM / 800K OTPM ($400 + 14 days) |
| **Gemini API** | Free tier: 5–15 RPM, 250K TPM | Tier 1 paid: 150–300 RPM, 1–2M TPM | Tier 3: 4K+ RPM, 4M+ TPM ($1K + 30 days) |

Key point: **Anthropic is the stingiest at entry tier.** That “Opus ate my whole limit in one prompt” experience you had is real — Tier 1 Opus allows only **80K output tokens per minute**, so one long generation can saturate it. Tier 1 Sonnet is even worse at **8K OTPM**.

OpenAI and Gemini are more generous at the low end. Gemini’s free tier alone is usable for Flash models.

---

### What $200/month buys in API tokens

Rough token volumes if you spent the whole $200 on one model class:

| Model class | Approx. output tokens per $200 |
|---|---|
| GPT-5.5 / Claude Sonnet / Gemini Pro | ~10–15 million output tokens |
| Claude Opus | ~5–7 million output tokens |
| GPT-4o-mini / Gemini Flash / Claude Haiku | ~100–300 million output tokens |

So $200 of API credits is **a lot of coding/research use** on mid-tier models, but it is not “unlimited frontier.” If you actually hammer GPT-5.5 or Opus all day, you can burn through $200 quickly.

---

### Practical comparison

**Anthropic Claude API**
- Pros: Excellent code quality; prompt caching reduces effective ITPM (cached reads don’t count); batch API at 50% cost.
- Cons: Lowest starting limits; Opus is expensive and low-OTPM; tier upgrades require both spend **and** waiting days.
- Best for: Code-heavy workflows where you can use Sonnet mostly and Opus sparingly.

**Google Gemini API**
- Pros: Generous free tier for Flash; cheapest paid pricing; 1M context window; fast tier progression.
- Cons: Pro models paid-only; reliability/context-loss complaints in long chats.
- Best for: High-volume mixed workloads, long-context tasks, cost-sensitive coding.

**OpenAI API**
- Pros: Highest top-tier throughput; strong model lineup; Codex/agent APIs.
- Cons: You’re currently banned, so this may not be an option.
- Best for: If you could use it, it’s the closest API to the ChatGPT Pro experience.

---

### Bottom line

If you replace ChatGPT Pro with API access:
- You will **not** get unlimited frontier-model use.
- You will get **much more transparency and control** over what you spend.
- For coding/research, the best value is probably a **mix**: cheap fast models (Gemini Flash / Claude Haiku / GPT-mini) for routine work, plus Sonnet/GPT-5.5 for hard tasks, plus tiny amounts of Opus only when needed.

Given your ban and your usage pattern, **Gemini API + Claude API combo** might be the cleanest replacement: Gemini for volume and long context, Claude for code quality. Both can be funded with prepaid credits, no subscription needed.

Want me to model a specific monthly budget split, or look at how OpenCode/ACP could route between them by task?

Summary: High prompt-cache reuse.

Output:
Important framing first: **ChatGPT Pro ($200) is a chat-app subscription. It does not include OpenAI API credits.** API usage is a separate, metered billing system. Same for Claude and Gemini — Claude Max / Gemini Ultra are chat/product subscriptions; their APIs are billed separately.

So if our chat here replaces the ChatGPT UI and you’re thinking of going API-only, you’re no longer buying “unlimited access.” You’re buying **metered tokens + rate limits**. Here is how the APIs actually compare.

---

### API rate limits (what you hit first)

| Provider | Entry tier | Frontier model limits | Top tier |
|---|---|---|---|
| **OpenAI API** | Tier 1: 500 RPM, ~200K TPM (after $5) | GPT-5.5: ~500 RPM, 500K TPM at Tier 1 | Tier 5: 10K RPM, 30M TPM ($1K spent + 30 days) |
| **Anthropic API** | Tier 1: 50 RPM, Opus 500K ITPM / 80K OTPM | Sonnet: 30K ITPM / 8K OTPM at Tier 1 | Tier 4: 4K RPM, Opus 10M ITPM / 800K OTPM ($400 + 14 days) |
| **Gemini API** | Free tier: 5–15 RPM, 250K TPM | Tier 1 paid: 150–300 RPM, 1–2M TPM | Tier 3: 4K+ RPM, 4M+ TPM ($1K + 30 days) |

Key point: **Anthropic is the stingiest at entry tier.** That “Opus ate my whole limit in one prompt” experience you had is real — Tier 1 Opus allows only **80K output tokens per minute**, so one long generation can saturate it. Tier 1 Sonnet is even worse at **8K OTPM**.

OpenAI and Gemini are more generous at the low end. Gemini’s free tier alone is usable for Flash models.

---

### What $200/month buys in API tokens

Rough token volumes if you spent the whole $200 on one model class:

| Model class | Approx. output tokens per $200 |
|---|---|
| GPT-5.5 / Claude Sonnet / Gemini Pro | ~10–15 million output tokens |
| Claude Opus | ~5–7 million output tokens |
| GPT-4o-mini / Gemini Flash / Claude Haiku | ~100–300 million output tokens |

So $200 of API credits is **a lot of coding/research use** on mid-tier models, but it is not “unlimited frontier.” If you actually hammer GPT-5.5 or Opus all day, you can burn through $200 quickly.

---

### Practical comparison

**Anthropic Claude API**
- Pros: Excellent code quality; prompt caching reduces effective ITPM (cached reads don’t count); batch API at 50% cost.
- Cons: Lowest starting limits; Opus is expensive and low-OTPM; tier upgrades require both spend **and** waiting days.
- Best for: Code-heavy workflows where you can use Sonnet mostly and Opus sparingly.

**Google Gemini API**
- Pros: Generous free tier for Flash; cheapest paid pricing; 1M context window; fast tier progression.
- Cons: Pro models paid-only; reliability/context-loss complaints in long chats.
- Best for: High-volume mixed workloads, long-context tasks, cost-sensitive coding.

**OpenAI API**
- Pros: Highest top-tier throughput; strong model lineup; Codex/agent APIs.
- Cons: You’re currently banned, so this may not be an option.
- Best for: If you could use it, it’s the closest API to the ChatGPT Pro experience.

---

### Bottom line

If you replace ChatGPT Pro with API access:
- You will **not** get unlimited frontier-model use.
- You will get **much more transparency and control** over what you spend.
- For coding/research, the best value is probably a **mix**: cheap fast models (Gemini Flash / Claude Haiku / GPT-mini) for routine work, plus Sonnet/GPT-5.5 for hard tasks, plus tiny amounts of Opus only when needed.

Given your ban and your usage pattern, **Gemini API + Claude API combo** might be the cleanest replacement: Gemini for volume and long context, Claude for code quality. Both can be funded with prepaid credits, no subscription needed.

Want me to model a specific monthly budget split, or look at how OpenCode/ACP could route between them by task?

Let's investigate how much API use subscribers are actually getting and if the limits are better in Claude or Gemini. I don't consider the chat interface for now, our chat here will replace that. Also not considering the nice cloud and other Google stuff in their plan. Just API use


Yes, Google has a coding agent equivalent, but the situation is more fragmented than ChatGPT/Codex or Claude/Claude Code.

**Google's coding agents:**

| Tool | What it is | Cost |
|---|---|---|
| **Gemini Code Assist** | IDE extension (VS Code, JetBrains, etc.) for code completion, generation, chat | Free Individuals edition; Standard/Enterprise ~$19–45/user/mo |
| **Gemini CLI / Antigravity CLI** | Terminal agent (replaced Gemini CLI for consumers on June 18, 2026) | Free tier with Google account, or via Google AI Pro/Ultra / API |
| **Antigravity** | New consumer/agentic layer announced at I/O 2026 | Free–$200 tiers |

So unlike OpenAI (Codex bundled with ChatGPT) and Anthropic (Claude Code bundled with Claude Pro), Google splits things:
- **Gemini Code Assist** is the IDE-native coding assistant.
- **Antigravity/Gemini CLI** is the terminal agent.
- They’re not automatically bundled into one subscription the same way.

---

**Answering your earlier questions directly:**

**Is Gemini better than Kimi for coding and research?**

- **Coding**: Kimi K2.6/K2.7 Code is generally **better than or equal to Gemini** on real-world software engineering (SWE-Bench Pro, agentic coding). Gemini 3.1 Pro edges ahead on reasoning and multimodal, but users report Kimi follows instructions better for code. Gemini is faster and has a 1M context window; Kimi is slower but cheaper and often more capable on hard coding tasks.
- **Research**: **Gemini wins** on long-context and multimodal because of the 1M-token window. Kimi has strong deep-search capabilities but smaller context.

**Are the top Chinese models close to Gemini?**

Yes. **Kimi (Moonshot)** is the closest — trading blows with Gemini 3.1 Pro, beating it on coding agents, losing slightly on pure reasoning/multimodal. **DeepSeek V4** is competitive too but users report more inconsistency. So the top Chinese models are **very close to Gemini**, closer than many people assume.

**On the OpenAI ban / new account / chargeback:**

- Creating a new account with the same payment info risks automatic linking and ban.
- You could do a chargeback if they ban you and keep the money, but they’ll likely blacklist your payment method/email/phone.
- The cleaner move is to build around non-OpenAI options for now.

---

**Bottom line for replacing ChatGPT Pro:**

If you want the closest equivalent to “ChatGPT Pro + Codex” without OpenAI:

- **Claude Max 5x/20x** → Claude Code included, best code quality, but tight limits.
- **Gemini Ultra + Antigravity + Code Assist** → broadest ecosystem, long context, but the coding agent is split across tools.
- **Kimi Code platform** → best price/performance for pure coding, especially via OpenCode or `pi.dev`.

Given your usage, a **Kimi + Gemini** combo might actually be ideal: Kimi for hard coding work, Gemini for research/long context. Both avoid the OpenAI ban issue entirely.

Summary: High prompt-cache reuse.

Output:
Yes, Google has a coding agent equivalent, but the situation is more fragmented than ChatGPT/Codex or Claude/Claude Code.

**Google's coding agents:**

| Tool | What it is | Cost |
|---|---|---|
| **Gemini Code Assist** | IDE extension (VS Code, JetBrains, etc.) for code completion, generation, chat | Free Individuals edition; Standard/Enterprise ~$19–45/user/mo |
| **Gemini CLI / Antigravity CLI** | Terminal agent (replaced Gemini CLI for consumers on June 18, 2026) | Free tier with Google account, or via Google AI Pro/Ultra / API |
| **Antigravity** | New consumer/agentic layer announced at I/O 2026 | Free–$200 tiers |

So unlike OpenAI (Codex bundled with ChatGPT) and Anthropic (Claude Code bundled with Claude Pro), Google splits things:
- **Gemini Code Assist** is the IDE-native coding assistant.
- **Antigravity/Gemini CLI** is the terminal agent.
- They’re not automatically bundled into one subscription the same way.

---

**Answering your earlier questions directly:**

**Is Gemini better than Kimi for coding and research?**

- **Coding**: Kimi K2.6/K2.7 Code is generally **better than or equal to Gemini** on real-world software engineering (SWE-Bench Pro, agentic coding). Gemini 3.1 Pro edges ahead on reasoning and multimodal, but users report Kimi follows instructions better for code. Gemini is faster and has a 1M context window; Kimi is slower but cheaper and often more capable on hard coding tasks.
- **Research**: **Gemini wins** on long-context and multimodal because of the 1M-token window. Kimi has strong deep-search capabilities but smaller context.

**Are the top Chinese models close to Gemini?**

Yes. **Kimi (Moonshot)** is the closest — trading blows with Gemini 3.1 Pro, beating it on coding agents, losing slightly on pure reasoning/multimodal. **DeepSeek V4** is competitive too but users report more inconsistency. So the top Chinese models are **very close to Gemini**, closer than many people assume.

**On the OpenAI ban / new account / chargeback:**

- Creating a new account with the same payment info risks automatic linking and ban.
- You could do a chargeback if they ban you and keep the money, but they’ll likely blacklist your payment method/email/phone.
- The cleaner move is to build around non-OpenAI options for now.

---

**Bottom line for replacing ChatGPT Pro:**

If you want the closest equivalent to “ChatGPT Pro + Codex” without OpenAI:

- **Claude Max 5x/20x** → Claude Code included, best code quality, but tight limits.
- **Gemini Ultra + Antigravity + Code Assist** → broadest ecosystem, long context, but the coding agent is split across tools.
- **Kimi Code platform** → best price/performance for pure coding, especially via OpenCode or `pi.dev`.

Given your usage, a **Kimi + Gemini** combo might actually be ideal: Kimi for hard coding work, Gemini for research/long context. Both avoid the OpenAI ban issue entirely.

Maybe I confused the terms, but Chatgpt includes codex use, and Claude includes Claude code use. Same with Gemini? Please answer earlier questions


You’re right — Google’s marketing is frustratingly vague. “Most generous quota, refreshed every five hours” sounds good until you see what users actually experience.

---

### What user reviews say about Google AI Ultra/Pro

**The quota is not really transparent:**
- Google advertises a **5-hour refresh**, but users report a **hidden weekly cap** on top of it.
- Ultra users say they hit the wall in **30–40 minutes of heavy Opus use**, then face **multi-day lockouts** (1–5 days) even on the $200 plan.
- The dashboard shows a single percentage, but the real budget is reverse-engineered from usage patterns. People call it a “bait and switch.”
- Google has silently reduced limits multiple times without notice.

**What actually works in Antigravity:**
- Gemini 3.1 Pro/Flash are decent and fast.
- 1M-token context is real and useful.
- Flash-Lite prompts are free and don’t count against quota.
- Good if you mix coding with research, video, YouTube Premium, storage.

**What hurts productivity:**
- Claude Opus inside Antigravity burns quota fast and gets locked out.
- New model versions (e.g., Opus 4.7) have lagged behind native Claude.
- “High traffic” errors turning into multi-day cooldowns mid-workflow.

---

### What user reviews say about Claude Max

**More predictable:**
- Limits refresh every 5 hours, and users report **no hidden weekly ceiling** on Max plans.
- Max 20x reportedly gives roughly **900 Opus messages per 5-hour window**.
- You can see where you stand; less “why am I locked out?”

**Better for deep coding work:**
- Consistent reports that Claude produces fewer syntax errors and needs fewer follow-up prompts.
- Opus 4.7 available sooner than in Antigravity.
- Users migrating from Google Ultra to Claude Max specifically for reliability.

** Downsides:**
- 200K context window vs Google’s 1M.
- Slower than Antigravity.
- Some users still burn through Max 20x in heavy sessions, but the lockout is a known 5-hour wait, not a surprise multi-day one.

---

### Direct answer: where do you get more done per dollar?

| If your main work is… | Better choice |
|---|---|
| **Coding / refactoring / architecture / multi-step reasoning** | **Claude Max** — more reliable, better code quality, predictable limits |
| **Research / long documents / 1M-context tasks / multimodal** | Google AI Ultra — when it works, the context and speed are unmatched |
| **Heavy daily frontier-model use** | **Claude Max 20x** — user reports consistently show fewer lockouts and more actual work hours |
| **Mixed use where you can tolerate quota roulette** | Google AI Ultra 20x |

### My recommendation

Since you already have **Kimi for coding**, and you want a second subscription that actually lets you work without hitting invisible walls:

**Get Claude Max 5x ($100) or 20x ($200).** For heavy use, 20x is the safer bet. It’s the closest productive replacement to ChatGPT Pro in terms of “sit down and actually get work done,” and user sentiment is strongly in its favor over Google Ultra for serious coding/reasoning.

Only go Google AI Ultra if you specifically need the **1M context** or the bundled Google perks, and you’re willing to deal with quota unpredictability. For raw getting-things-done, Claude wins right now.

Summary: High prompt-cache reuse.

Output:
You’re right — Google’s marketing is frustratingly vague. “Most generous quota, refreshed every five hours” sounds good until you see what users actually experience.

---

### What user reviews say about Google AI Ultra/Pro

**The quota is not really transparent:**
- Google advertises a **5-hour refresh**, but users report a **hidden weekly cap** on top of it.
- Ultra users say they hit the wall in **30–40 minutes of heavy Opus use**, then face **multi-day lockouts** (1–5 days) even on the $200 plan.
- The dashboard shows a single percentage, but the real budget is reverse-engineered from usage patterns. People call it a “bait and switch.”
- Google has silently reduced limits multiple times without notice.

**What actually works in Antigravity:**
- Gemini 3.1 Pro/Flash are decent and fast.
- 1M-token context is real and useful.
- Flash-Lite prompts are free and don’t count against quota.
- Good if you mix coding with research, video, YouTube Premium, storage.

**What hurts productivity:**
- Claude Opus inside Antigravity burns quota fast and gets locked out.
- New model versions (e.g., Opus 4.7) have lagged behind native Claude.
- “High traffic” errors turning into multi-day cooldowns mid-workflow.

---

### What user reviews say about Claude Max

**More predictable:**
- Limits refresh every 5 hours, and users report **no hidden weekly ceiling** on Max plans.
- Max 20x reportedly gives roughly **900 Opus messages per 5-hour window**.
- You can see where you stand; less “why am I locked out?”

**Better for deep coding work:**
- Consistent reports that Claude produces fewer syntax errors and needs fewer follow-up prompts.
- Opus 4.7 available sooner than in Antigravity.
- Users migrating from Google Ultra to Claude Max specifically for reliability.

** Downsides:**
- 200K context window vs Google’s 1M.
- Slower than Antigravity.
- Some users still burn through Max 20x in heavy sessions, but the lockout is a known 5-hour wait, not a surprise multi-day one.

---

### Direct answer: where do you get more done per dollar?

| If your main work is… | Better choice |
|---|---|
| **Coding / refactoring / architecture / multi-step reasoning** | **Claude Max** — more reliable, better code quality, predictable limits |
| **Research / long documents / 1M-context tasks / multimodal** | Google AI Ultra — when it works, the context and speed are unmatched |
| **Heavy daily frontier-model use** | **Claude Max 20x** — user reports consistently show fewer lockouts and more actual work hours |
| **Mixed use where you can tolerate quota roulette** | Google AI Ultra 20x |

### My recommendation

Since you already have **Kimi for coding**, and you want a second subscription that actually lets you work without hitting invisible walls:

**Get Claude Max 5x ($100) or 20x ($200).** For heavy use, 20x is the safer bet. It’s the closest productive replacement to ChatGPT Pro in terms of “sit down and actually get work done,” and user sentiment is strongly in its favor over Google Ultra for serious coding/reasoning.

Only go Google AI Ultra if you specifically need the **1M context** or the bundled Google perks, and you’re willing to deal with quota unpredictability. For raw getting-things-done, Claude wins right now.

Users on Google AI Ultra receive:

    The highest, most generous quota, refreshed every five hours
    Highest weekly rate limits
    Access to third-party models

Users on Google AI Pro receive:

    High, generous quota, refreshed every five hours until weekly limit reached
    Higher weekly rate limit

This is even worse than the abstract 5x and 20x

Where I will actually get more done for money, with Claude or antigravity/Google ai? Please consider user reviews.

Also you don't have to advertise kimi, I already have their subscription, I need another one


That price gap changes the calculation a lot.

At those German prices:
- **Claude Max 5x**: ~€137 (~$145)
- **Claude Max 20x**: ~€275 (~$290)
- **Google AI Ultra**: ~€100 (~$105)

Google AI Ultra is now roughly **half the price of Claude Max 20x** and **25% cheaper than Claude Max 5x**. Even if Google’s limits are flaky, you can afford to hit the wall and still come out ahead if it works even 50–60% of the time.

---

### Reframed recommendation

| If you… | Best pick |
|---|---|
| Need reliable, heavy daily coding and can afford the premium | Claude Max 20x (but you’re paying a lot for predictability) |
| Want the most raw usage per euro and can work around quota hiccups | **Google AI Ultra at €100** |
| Are unsure about your actual usage level | Claude Max 5x or Google AI Pro as a trial month |

Given the German pricing, I’d actually lean toward trying **Google AI Ultra for one month** first. Reasons:

- It’s cheap enough that even frustrating lockouts are tolerable.
- You already have **Kimi** as your reliable coding fallback, so you won’t be dead in the water when Google throttles you.
- You can test whether your real workflow hits the hidden weekly cap or not.
- If it turns out unusable, you’ve only spent €100 and can switch to Claude Max.

The main risk is productivity disruption mid-task. But since you have Kimi and this chat as backups, that risk is lower.

---

### Pragmatic plan

1. **Subscribe to Google AI Ultra (€100)** for one month.
2. Use it as your daily driver for chat/research and some coding.
3. Keep Kimi for heavy coding sessions, especially when Google locks you out.
4. Track how often you hit limits and how disruptive it is.
5. After a month, decide: stay with Google, upgrade to Claude Max 20x if you need more reliability, or keep the combo.

Does that sound reasonable, or do you want me to dig deeper into whether Google AI Ultra’s limits have stabilized recently?

Summary: Moderate prompt-cache reuse.

Output:
That price gap changes the calculation a lot.

At those German prices:
- **Claude Max 5x**: ~€137 (~$145)
- **Claude Max 20x**: ~€275 (~$290)
- **Google AI Ultra**: ~€100 (~$105)

Google AI Ultra is now roughly **half the price of Claude Max 20x** and **25% cheaper than Claude Max 5x**. Even if Google’s limits are flaky, you can afford to hit the wall and still come out ahead if it works even 50–60% of the time.

---

### Reframed recommendation

| If you… | Best pick |
|---|---|
| Need reliable, heavy daily coding and can afford the premium | Claude Max 20x (but you’re paying a lot for predictability) |
| Want the most raw usage per euro and can work around quota hiccups | **Google AI Ultra at €100** |
| Are unsure about your actual usage level | Claude Max 5x or Google AI Pro as a trial month |

Given the German pricing, I’d actually lean toward trying **Google AI Ultra for one month** first. Reasons:

- It’s cheap enough that even frustrating lockouts are tolerable.
- You already have **Kimi** as your reliable coding fallback, so you won’t be dead in the water when Google throttles you.
- You can test whether your real workflow hits the hidden weekly cap or not.
- If it turns out unusable, you’ve only spent €100 and can switch to Claude Max.

The main risk is productivity disruption mid-task. But since you have Kimi and this chat as backups, that risk is lower.

---

### Pragmatic plan

1. **Subscribe to Google AI Ultra (€100)** for one month.
2. Use it as your daily driver for chat/research and some coding.
3. Keep Kimi for heavy coding sessions, especially when Google locks you out.
4. Track how often you hit limits and how disruptive it is.
5. After a month, decide: stay with Google, upgrade to Claude Max 20x if you need more reliability, or keep the combo.

Does that sound reasonable, or do you want me to dig deeper into whether Google AI Ultra’s limits have stabilized recently?

Unfortunately it's more like 137 and 275 euros for Claude 5x and 20x here in Germany. While Google ai ultra is 100 euros. That's why it's hard to tell


Here is the fuller picture on Google AI Ultra.

---

### What Google AI Ultra includes

**Models available:**
- **Gemini 3.1 Pro / Gemini 3 Pro** — Google’s flagship models, 1M context window, 64K output tokens via API (but see below).
- **Gemini 3.5 Flash** — fast, cheaper, good for lighter tasks.
- **Nano Banana Pro** — image/video model.
- **Claude Opus 4.6 / Sonnet 4.6** — third-party models inside Antigravity.
- **Gemini 3 Pro Deep Think** — reasoning mode.
- **Deep Research** on Gemini 3.
- **Veo 3.1 Lite** — video generation (limited trial).
- **Project Mariner** — agentic web browsing.
- Non-AI perks: 30 TB storage, YouTube Premium, $100/mo Google Cloud credit (on some plans).

**Important caveat:** Access to third-party models and newer model versions inside Antigravity has lagged. For example, Claude Opus 4.7 was released but Antigravity stayed on 4.6 for a while.

---

### Can you use it in OpenCode?

**Yes, but not with a regular API key.**

- Google AI Ultra is **OAuth-based through Antigravity**, not a traditional API key from AI Studio.
- There are **community OpenCode plugins** like `opencode-antigravity-auth` that let OpenCode authenticate to Antigravity via Google OAuth and use the models:
  - `google/antigravity-claude-opus-4-6-thinking`
  - `google/antigravity-claude-sonnet-4-6`
  - `google/antigravity-gemini-3.1-pro`
  - `google/antigravity-gemini-3.5-flash`
- These plugins route through your Google AI Ultra subscription quotas.
- You **cannot** simply plug a Gemini API key into Antigravity. Google has explicitly said there is no “bring your own key” setup for Antigravity CLI/IDE.

So OpenCode integration is possible, but it depends on a third-party OAuth plugin and Google’s Antigravity backend.

---

### What users report: limits and output quality

**Limits — mixed and often frustrating:**

- Some Ultra users report **never hitting limits** with moderate daily use.
- Many others report **hitting the wall in 30–60 minutes of heavy Opus use**, then waiting **hours to days**.
- Google advertises a **5-hour refresh**, but users strongly suspect a **hidden weekly cap** that shrinks your per-window allowance as the week goes on.
- Quotas appear to be **“compute-based”** rather than transparent token counts, making them hard to predict.
- Google has **silently reduced limits multiple times** without clear notice.

**Output quality — depends heavily on model:**

| Model | User sentiment |
|---|---|
| **Claude Opus 4.6 in Antigravity** | Generally praised for coding quality, but burns quota fast. Some users report model-downgrade bugs (selecting Opus but getting Sonnet). |
| **Gemini 3.1 Pro** | Mixed. Some call it great for coding; others say it “talks to itself endlessly about tools,” is slow, self-truncates, or produces lower-quality code than Opus. A recent 3.1 Pro update seems to have helped output length. |
| **Gemini 3.5 Flash** | Often preferred over 3.1 Pro for small, well-defined tasks because it’s fast and cheap. |
| **Gemini 3 Pro / Deep Think** | Good for reasoning, but slow. |

**Hard output-token cap in Antigravity:**
- Antigravity appears to cap output at **16,384 tokens per turn** even though the underlying models support 64K+.
- For Claude, `max_thinking_length` is reportedly capped at **1,024 tokens**.
- This is a significant limitation for agentic coding where long outputs matter.

---

### Bottom line

Google AI Ultra at €100 is a **good value on paper**, especially with the bundled perks. But for serious coding/agentic work, user reports are very mixed:

- **Pros:** cheap, fast Gemini models, 1M context, Claude access, lots of bundled extras.
- **Cons:** opaque quotas, hidden weekly caps, output-token limits, model-version lag, quality inconsistency.

If your main use is **chat + research + lighter coding**, it’s probably great. If you want **deep agentic coding like Codex/Claude Code**, the Antigravity platform is still rougher and more unpredictable than native Claude Code.

Given that you already have Kimi for coding, Google AI Ultra could work well as your **second subscription for chat, research, and quick tasks** — but I’d keep expectations modest for replacing Claude Code fully.

Summary: High prompt-cache reuse.

Output:
Here is the fuller picture on Google AI Ultra.

---

### What Google AI Ultra includes

**Models available:**
- **Gemini 3.1 Pro / Gemini 3 Pro** — Google’s flagship models, 1M context window, 64K output tokens via API (but see below).
- **Gemini 3.5 Flash** — fast, cheaper, good for lighter tasks.
- **Nano Banana Pro** — image/video model.
- **Claude Opus 4.6 / Sonnet 4.6** — third-party models inside Antigravity.
- **Gemini 3 Pro Deep Think** — reasoning mode.
- **Deep Research** on Gemini 3.
- **Veo 3.1 Lite** — video generation (limited trial).
- **Project Mariner** — agentic web browsing.
- Non-AI perks: 30 TB storage, YouTube Premium, $100/mo Google Cloud credit (on some plans).

**Important caveat:** Access to third-party models and newer model versions inside Antigravity has lagged. For example, Claude Opus 4.7 was released but Antigravity stayed on 4.6 for a while.

---

### Can you use it in OpenCode?

**Yes, but not with a regular API key.**

- Google AI Ultra is **OAuth-based through Antigravity**, not a traditional API key from AI Studio.
- There are **community OpenCode plugins** like `opencode-antigravity-auth` that let OpenCode authenticate to Antigravity via Google OAuth and use the models:
  - `google/antigravity-claude-opus-4-6-thinking`
  - `google/antigravity-claude-sonnet-4-6`
  - `google/antigravity-gemini-3.1-pro`
  - `google/antigravity-gemini-3.5-flash`
- These plugins route through your Google AI Ultra subscription quotas.
- You **cannot** simply plug a Gemini API key into Antigravity. Google has explicitly said there is no “bring your own key” setup for Antigravity CLI/IDE.

So OpenCode integration is possible, but it depends on a third-party OAuth plugin and Google’s Antigravity backend.

---

### What users report: limits and output quality

**Limits — mixed and often frustrating:**

- Some Ultra users report **never hitting limits** with moderate daily use.
- Many others report **hitting the wall in 30–60 minutes of heavy Opus use**, then waiting **hours to days**.
- Google advertises a **5-hour refresh**, but users strongly suspect a **hidden weekly cap** that shrinks your per-window allowance as the week goes on.
- Quotas appear to be **“compute-based”** rather than transparent token counts, making them hard to predict.
- Google has **silently reduced limits multiple times** without clear notice.

**Output quality — depends heavily on model:**

| Model | User sentiment |
|---|---|
| **Claude Opus 4.6 in Antigravity** | Generally praised for coding quality, but burns quota fast. Some users report model-downgrade bugs (selecting Opus but getting Sonnet). |
| **Gemini 3.1 Pro** | Mixed. Some call it great for coding; others say it “talks to itself endlessly about tools,” is slow, self-truncates, or produces lower-quality code than Opus. A recent 3.1 Pro update seems to have helped output length. |
| **Gemini 3.5 Flash** | Often preferred over 3.1 Pro for small, well-defined tasks because it’s fast and cheap. |
| **Gemini 3 Pro / Deep Think** | Good for reasoning, but slow. |

**Hard output-token cap in Antigravity:**
- Antigravity appears to cap output at **16,384 tokens per turn** even though the underlying models support 64K+.
- For Claude, `max_thinking_length` is reportedly capped at **1,024 tokens**.
- This is a significant limitation for agentic coding where long outputs matter.

---

### Bottom line

Google AI Ultra at €100 is a **good value on paper**, especially with the bundled perks. But for serious coding/agentic work, user reports are very mixed:

- **Pros:** cheap, fast Gemini models, 1M context, Claude access, lots of bundled extras.
- **Cons:** opaque quotas, hidden weekly caps, output-token limits, model-version lag, quality inconsistency.

If your main use is **chat + research + lighter coding**, it’s probably great. If you want **deep agentic coding like Codex/Claude Code**, the Antigravity platform is still rougher and more unpredictable than native Claude Code.

Given that you already have Kimi for coding, Google AI Ultra could work well as your **second subscription for chat, research, and quick tasks** — but I’d keep expectations modest for replacing Claude Code fully.

Yes please read more on ai ultra, what it actually includes in terms of model use, can I use this in opencode, and what do users report regarding the limits and output quality


I think so too. At €100 it’s a low-risk experiment, and you have Kimi as a safety net if the limits get annoying.

A few things to do if you try it:

- Use **Gemini 3.1 Pro / Flash** for chat, research, and lighter coding.
- Use **Claude Opus inside Antigravity** sparingly for tasks where Kimi struggles.
- Install the **OpenCode Antigravity plugin** if you want to route it through OpenCode.
- Track how fast your quota burns in the first week so you can decide before the billing cycle renews.

If it works well, you’ve saved money. If it doesn’t, you’ve only spent €100 and can switch to Claude Max.

Want me to help set up the OpenCode plugin config, or are you planning to try it through the browser/Antigravity IDE first?

Summary: Moderate prompt-cache reuse.

Output:
I think so too. At €100 it’s a low-risk experiment, and you have Kimi as a safety net if the limits get annoying.

A few things to do if you try it:

- Use **Gemini 3.1 Pro / Flash** for chat, research, and lighter coding.
- Use **Claude Opus inside Antigravity** sparingly for tasks where Kimi struggles.
- Install the **OpenCode Antigravity plugin** if you want to route it through OpenCode.
- Track how fast your quota burns in the first week so you can decide before the billing cycle renews.

If it works well, you’ve saved money. If it doesn’t, you’ve only spent €100 and can switch to Claude Max.

Want me to help set up the OpenCode plugin config, or are you planning to try it through the browser/Antigravity IDE first?


Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize/cut** — users report Gemini 3.1 Pro sometimes self-truncates or gives you 20 lines when you asked for 200. This is especially annoying for long-form prose.
- **Less stylistic personality** than Claude — Claude tends to produce more distinctive, human-like prose; Gemini can feel flatter or more generic.
- **Context-loss bugs** in long chats — it can forget or prune earlier material, then deny it.

**For creative/long-form writing:** Claude is usually preferred.
**For drafting, editing, research summaries, technical writing:** Gemini is perfectly usable and fast.

So if you subscribe to Google AI Ultra, you’d probably use:
- **Gemini 3.1 Pro** for research, drafting, summaries, long-context tasks.
- **Claude Sonnet** for prose where style and quality matter.
- **Claude Opus** sparingly, for the hardest writing or coding problems.

Summary: High prompt-cache reuse.

Output:
Yes, **Claude Sonnet 4.6** is available inside Antigravity as a cheaper-on-quota alternative to Opus. It burns quota slower and is fine for most coding and writing tasks. Some users actually prefer Sonnet for routine work and save Opus only for hard problems.

---

### Gemini models with prose

Gemini is generally **good at prose**, but with caveats:

**Strengths:**
- **Long-context coherence** — the 1M-token window means it can hold a lot of narrative or document context.
- **Fast output** — good for drafting, brainstorming, summaries.
- **Multimodal** — can work with attached documents, images, audio alongside text.

**Weaknesses:**
- **Over-eager to summarize/cut** — users report Gemini 3.1 Pro sometimes self-truncates or gives you 20 lines when you asked for 200. This is especially annoying for long-form prose.
- **Less stylistic personality** than Claude — Claude tends to produce more distinctive, human-like prose; Gemini can feel flatter or more generic.
- **Context-loss bugs** in long chats — it can forget or prune earlier material, then deny it.

**For creative/long-form writing:** Claude is usually preferred.
**For drafting, editing, research summaries, technical writing:** Gemini is perfectly usable and fast.

So if you subscribe to Google AI Ultra, you’d probably use:
- **Gemini 3.1 Pro** for research, drafting, summaries, long-context tasks.
- **Claude Sonnet** for prose where style and quality matter.
- **Claude Opus** sparingly, for the hardest writing or coding problems.

Does it have sonnet too as a cheaper option? How are Gemini models with prose?


Yes, you really can use Google AI Ultra with OpenCode, but through a **community plugin**, not natively.

The plugin is **`opencode-antigravity-auth`** (or forks like `opencode-ag-auth`). It works by:

1. Authenticating OpenCode to **Antigravity via Google OAuth**.
2. Routing your OpenCode requests through Antigravity’s backend, consuming your Google AI Ultra subscription quota.
3. Exposing models like:
   - `google/antigravity-gemini-3.1-pro`
   - `google/antigravity-gemini-3.5-flash`
   - `google/antigravity-claude-opus-4-6-thinking`
   - `google/antigravity-claude-sonnet-4-6`

So from inside OpenCode, you just select the model and use it normally.

---

### Caveats

- It’s **third-party and unofficial** — not made by Google or Anthropic. Quality and maintenance depend on the plugin author.
- It routes through Antigravity, so all the quota and output-limit issues we discussed still apply.
- You can’t use a regular `GEMINI_API_KEY` from AI Studio for this. It needs OAuth to your Google account.
- Some versions of the plugin also support API-key fallbacks via `agy_sdk.cloud_projects` for extra capacity, but the primary path is OAuth.

---

### Should you stay with OpenCode?

Honestly, OpenCode is a good foundation. It lets you mix providers without being locked into one vendor’s UI. If this plugin works, you get:

- Google AI Ultra models inside your familiar workflow.
- Kimi as your coding fallback.
- Future option to add Claude Max via Anthropic API or subscription if needed.

That’s a flexible setup. No need to marry OpenCode forever, but it’s worth trying the plugin before switching tools.

Want me to look up the exact install/config steps for `opencode-antigravity-auth` so you can try it?

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yes, you really can use Google AI Ultra with OpenCode, but through a **community plugin**, not natively.

The plugin is **`opencode-antigravity-auth`** (or forks like `opencode-ag-auth`). It works by:

1. Authenticating OpenCode to **Antigravity via Google OAuth**.
2. Routing your OpenCode requests through Antigravity’s backend, consuming your Google AI Ultra subscription quota.
3. Exposing models like:
   - `google/antigravity-gemini-3.1-pro`
   - `google/antigravity-gemini-3.5-flash`
   - `google/antigravity-claude-opus-4-6-thinking`
   - `google/antigravity-claude-sonnet-4-6`

So from inside OpenCode, you just select the model and use it normally.

---

### Caveats

- It’s **third-party and unofficial** — not made by Google or Anthropic. Quality and maintenance depend on the plugin author.
- It routes through Antigravity, so all the quota and output-limit issues we discussed still apply.
- You can’t use a regular `GEMINI_API_KEY` from AI Studio for this. It needs OAuth to your Google account.
- Some versions of the plugin also support API-key fallbacks via `agy_sdk.cloud_projects` for extra capacity, but the primary path is OAuth.

---

### Should you stay with OpenCode?

Honestly, OpenCode is a good foundation. It lets you mix providers without being locked into one vendor’s UI. If this plugin works, you get:

- Google AI Ultra models inside your familiar workflow.
- Kimi as your coding fallback.
- Future option to add Claude Max via Anthropic API or subscription if needed.

That’s a flexible setup. No need to marry OpenCode forever, but it’s worth trying the plugin before switching tools.

Want me to look up the exact install/config steps for `opencode-antigravity-auth` so you can try it?

Can I really use Google ai ultra sub with opencode somehow? Maybe I shouldn't get married to opencode but it works well for me


For using Google AI Ultra in OpenCode, the **plugin is currently the best way**. There is no more direct official path because Google has deliberately closed the easy routes:

- **No BYO API key** for Antigravity — Google does not allow a regular Gemini API key to power Antigravity CLI/IDE.
- **No native OpenCode provider** for Google AI Ultra — OpenCode does not ship with Antigravity OAuth built in.

So your realistic options are:

| Option | Directness | Notes |
|---|---|---|
| **OpenCode + `opencode-antigravity-auth` plugin** | Indirect, but within OpenCode | Best if you want to stay in OpenCode. |
| **Antigravity CLI / IDE directly** | Official, but separate tool | More direct access to your subscription, but leaves OpenCode. |
| **Gemini API key + OpenCode native provider** | Direct for Gemini models only | Does **not** use your Ultra subscription quota; separate metered billing. |
| **Switch to Claude API / subscription** | Direct | Native OpenCode support, but different provider and price. |

---

### If you want it more direct

The most direct approach is probably:

1. **Use Antigravity CLI/IDE directly** for your Google AI Ultra subscription work.
2. Keep **OpenCode for Kimi** and anything else.
3. Run both side by side.

That avoids the unofficial plugin, gives you full access to Ultra quotas and features, and still keeps OpenCode in your stack.

The trade-off is you lose the single-tool workflow. But given how unstable the Antigravity integration looks, running it directly might actually be less friction than fighting a plugin.

Would you rather try the plugin first, or set up Antigravity CLI directly?

Summary: High prompt-cache reuse.

Output:
For using Google AI Ultra in OpenCode, the **plugin is currently the best way**. There is no more direct official path because Google has deliberately closed the easy routes:

- **No BYO API key** for Antigravity — Google does not allow a regular Gemini API key to power Antigravity CLI/IDE.
- **No native OpenCode provider** for Google AI Ultra — OpenCode does not ship with Antigravity OAuth built in.

So your realistic options are:

| Option | Directness | Notes |
|---|---|---|
| **OpenCode + `opencode-antigravity-auth` plugin** | Indirect, but within OpenCode | Best if you want to stay in OpenCode. |
| **Antigravity CLI / IDE directly** | Official, but separate tool | More direct access to your subscription, but leaves OpenCode. |
| **Gemini API key + OpenCode native provider** | Direct for Gemini models only | Does **not** use your Ultra subscription quota; separate metered billing. |
| **Switch to Claude API / subscription** | Direct | Native OpenCode support, but different provider and price. |

---

### If you want it more direct

The most direct approach is probably:

1. **Use Antigravity CLI/IDE directly** for your Google AI Ultra subscription work.
2. Keep **OpenCode for Kimi** and anything else.
3. Run both side by side.

That avoids the unofficial plugin, gives you full access to Ultra quotas and features, and still keeps OpenCode in your stack.

The trade-off is you lose the single-tool workflow. But given how unstable the Antigravity integration looks, running it directly might actually be less friction than fighting a plugin.

Would you rather try the plugin first, or set up Antigravity CLI directly?

Yeah, is it the best way to do it? Can it be more direct?


Good to hear it works. That aligns with what user reports said — Gemini 3.1 Pro is competitive with Kimi on coding but not clearly better. It’s more of a sidegrade than an upgrade.

Where Gemini should shine compared to Kimi is:
- **Long-context tasks** — 1M tokens vs 262K.
- **Multimodal** — images, video, PDFs, audio.
- **Speed** — especially Flash models.
- **Deep Research / web-grounded queries**.

For raw coding quality, Kimi K2.6/K2.7 is already near the top, so Gemini 3.1 Pro having trouble outperforming it isn’t surprising.

Have you tried the Claude models through the plugin yet, or mostly Gemini so far?

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Good to hear it works. That aligns with what user reports said — Gemini 3.1 Pro is competitive with Kimi on coding but not clearly better. It’s more of a sidegrade than an upgrade.

Where Gemini should shine compared to Kimi is:
- **Long-context tasks** — 1M tokens vs 262K.
- **Multimodal** — images, video, PDFs, audio.
- **Speed** — especially Flash models.
- **Deep Research / web-grounded queries**.

For raw coding quality, Kimi K2.6/K2.7 is already near the top, so Gemini 3.1 Pro having trouble outperforming it isn’t surprising.

Have you tried the Claude models through the plugin yet, or mostly Gemini so far?

Yeah I installed it and using currently. It works fine, although Gemini 3.1 pro seems comparable to kimi and not any better.