Opus 4.6 is nuts. Everything I throw at it works. Frontend, backend, algorithms—it does not matter.
I start with a PRD, ask for a step-by-step plan, and just execute on each step at a time. Sometimes ideas are dumb, but checking and guiding step by step helps it ship working things in hours.
It was also the first AI I felt, "Damn, this thing is smarter than me."
The other crazy thing is that with today's tech, these things can be made to work at 1k tokens/sec with multiple agents working at the same time, each at that speed.
Opus 4.6 is AGI in my book. They won’t admit it, but it’s absolutely true. It shows initiative in not only getting things right but also adding improvements that the original prompt didn't request that match the goals of the job.
Opus helped me brick my RPi CM4 today. It glibly apologized for telling to use an e instead of a 6 in a boot loader sequence.
Spent an hour or so unraveling the mess. My feeling are growing more and more conflicted about these tools. They are here to stay obviously.
I’m honestly uncertain about the junior engineers I’m working with who are more productive than they might be otherwise, but are gaining zero (or very little) experience. It’s like the future is a world where the entire programming sphere is dominated by the clueless non technical management that we’ve all had to deal with in small proportion a time or two.
I’ll put out a suggestion you pair with codex or deepthink for audit and review - opus is still prone to … enthusiastic architectural decisions. I promise you will be at least thankful and at most like ‘wtf?’ at some audit outputs.
Also shout out to beads - I highly recommend you pair it with beads from yegge: opus can lay out a large project with beads, and keep track of what to do next and churn through the list beautifully with a little help.
> Standard pricing now applies across the full 1M window for both models, with no long-context premium. Media limits expand to 600 images or PDF pages.
For Claude Code users this is huge - assuming coherence remains strong past 200k tok.
Is it ever useful to have a context window that full? I try to keep usage under 40%, or about 80k tokens, to avoid what Dex Horthy calls the dumb zone in his research-plan-implement approach. Works well for me so far.
I'd been on Codex for a while and with Codex 5.2 I:
1) No longer found the dumb zone
2) No longer feared compaction
Switching to Opus for stupid political reasons, I still have not had the dumb zone - but I'm back to disliking compaction events and so the smaller context window it has, has really hurt.
I hope they copy OpenAI's compaction magic soon, but I am also very excited to try the longer context window.
For me, it's less about being able to look back -800k tokens. It's about being able to flow a conversation for a lot longer without forcing compaction. Generally, I really only need the most recent ~50k tokens, but having the old context sitting around is helpful.
● RESEARCH. Don't code yet. Let the agent scan the files first. Docs lie. Code doesn't.
● PLAN. The agent writes a detailed step-by-step plan. You review and approve the plan, not just the output. Dex calls this avoiding "outsourcing your thinking." The plan is where intent gets compressed before execution starts.
● IMPLEMENT. Execute in a fresh context window.
The meta-principle he calls Frequent Intentional Compaction: don't let the chat run long. Ask the agent to summarize state, open a new chat with that summary, keep the model in the smart zone.
When running long autonomous tasks it is quite frequent to fill the context, even several times. You are out of the loop so it just happens if Claude goes a bit in circles, or it needs to iterate over CI reds, or the task was too complex. I'm hoping a long context > small context + 2 compacts.
Since I'm yet to seriously dive into vibe coding or AI-assisted coding, does the IDE experience offer tracking a tally of the context size? (So you know when you're getting close or entering the "dumb zone")?
It's kind of like having a 16 gallon gas tank in your car versus a 4 gallon tank. You don't need the bigger one the majority of the time, but the range anxiety that comes with the smaller one and annoyance when you DO need it is very real.
After running a context window up high, probably near 70% on opus 4.6 High and watching it take 20% bites out of my 5hr quota per prompt I've been experimenting with dumping context after completing a task. Seems to be working ok. I wonder if I was running into the long context premium. Would that apply to Pro subs or is just relevant to api pricing?
I mean, try using copilot on any substantial back-end codebase and watch it eat 90+% just building a plan/checklist. Of course copilot is constrained to 120k I believe? So having 10x that will blow open up some doors that have been closed for me in my work so far.
That said, 120k is pleeenty if you’re just building front-end components and have your API spec on hand already.
I've been using the 1M window at work through our enterprise plan as I'm beginning to adopt AI in my development workflow (via Cline). It seems to have been holding up pretty well until about 700k+. Sometimes it would continue to do okay past that, sometimes it started getting a bit dumb around there.
(Note that I'm using it in more of a hands-on pair-programming mode, and not in a fully-automated vibecoding mode.)
Well, the question is what is contributing to the usage. Because as the context grows, the amount of input tokens are increasing. A model call with 800K token as input is 8 times more expensive than a model call with 100K tokens as input. Especially if we resume a conversation and caching does not hit, it would be very expensive with API pricing.
The quality with the 1M window has been very poor for me, specifically for coding tasks. It constantly forgets stuff that has happened in the existing conversation. n=1, ymmv
Yes, especially with shifts in focus of a long conversation. But given the high error rates of Opus 4.6 the last few weeks it is possibly due to other factors. Conversational and code prodding has been essential.
Is there a writeup anywhere on what this means for effective context? I think that many of us have found that even when the context window was 100k tokens the actual usable window was smaller than that. As you got closer to 100k performance degraded substantially. I'm assuming that is still true but what does the curve look like?
> As you got closer to 100k performance degraded substantially
In practice, I haven't found this to be the case at all with Claude Code using Opus 4.6. So maybe it's another one of those things that used to be true, and now we all expect it to be true.
And of course when we expect something, we'll find it, so any mistakes at 150k context use get attributed to the context, while the same mistake at 50k gets attributed to the model.
I mentioned this at work but context still rots at the same rate. 90k tokens consumed has just as bad results in 100k context window or 1M.
Personally, I’m on a 6M+ line codebase and had no problems with the old window. I’m not sending it blindly into the codebase though like I do for small projects. Good prompts are necessary at scale.
Isn't transformer attention quadratic in complexity in terms of context size? In order to achieve 1M token context I think these models have to be employing a lot of shortcuts.
I'm not an expert but maybe this explains context rot.
Nope, there’s no tricks unless there’s been major architectural shifts I missed. The rot doesn’t come from inference tricks to try to bring down quadratic complexity of the KV cache. Task performance problems are generally a training problem - the longer and larger the data set, the fewer examples you have to train on it. So how do you train the model to behave well - that’s where the tricks are. I believe most of it relies on synthetically generated data if I’m not mistaken, which explains the rot.
Claude Code 2.1.75 now no longer delineates between base Opus and 1M Opus: it's the same model. Oddly, I have Pro where the change supposedly only for Max+ but am still seeing this to be case.
EDIT: Don't think Pro has access to it, a typical prompt just hit the context limit.
The removal of extra pricing beyond 200k tokens may be Anthropic's salvo in the agent wars against GPT 5.4's 1M window and extra pricing for that.
Those sorts of volume discounts are what you do when you're trying to incentivize more consumption. Anthropic already has more demand then they're logistically able to serve, at the moment (look at their uptime chart, it's barely even 1 9 of reliability). For them, 1 user consuming 5 units of compute is less attractive than 5 users consuming 1 unit.
They would probably implement _diminishing_-value pricing if pure pricing efficiency was their only concern.
5x Max is the plan I use because the Pro plan limits out so quickly. I don't use Claude full-time, but I do need Claude Code, and I do prefer to use Opus for everything because it's focused and less chatty.
This is super exciting. I've been poking at it today, and it definitely changes my workflow -- I feel like a full three or four hour parallel coding session with subagents is now generally fitting into a single master session.
The stats claim Opus at 1M is about like 5.4 at 256k -- these needle long context tests don't always go with quality reasoning ability sadly -- but this is still a significant improvement, and I haven't seen dramatic falloff in my tests, unlike q4 '25 models.
p.s. what's up with sonnet 4.5 getting comparatively better as context got longer?
Did it get better? I used sonnet 4.5 1m frequently and my impression was that it was around the same performance but a hell of a lot faster since the 1m model was willing to spends more tokens at each step vs preferring more token-cautious tool calls.
I've been using Opus 4.5 for programmatic SEO and localizing game descriptions. If 4.6 truly improves context compaction, it could significantly lower the API costs for large-scale content generation. Has anyone tested its logic consistency on JSON output compared to 4.5?
Do long sessions also burn through token budgets much faster?
If the chat client is resending the whole conversation each turn, then once you're deep into a session every request already includes tens of thousands of tokens of prior context. So a message at 70k tokens into a conversation is much "heavier" than one at 2k (at least in terms of input tokens). Yes?
That's correct. Input caching helps, but even then at e.g. 800k tokens with all of them cached, the API price is $0.50 * 0.8 = $0.40 per request, which adds up really fast. A "request" can be e.g. a single tool call response, so you can easily end up making many $0.40 requests per minute.
Compared to yesterday my Claude Max subscription burns usage like absolutely crazy (13% of weekly usage from fresh reset today with just a handful prompts on two new C++ projects, no deps) and has become unbearably slow (as in 1hr for a prompt response). GGWP Anthropic, it was great while it lasted but this isn't worth the hundreds of dollars.
Awesome.... With Sonnet 4.5, I had Cline soft trigger compaction at 400k (it wandered off into the weeds at 500k). But the stability of the 4.6 models is notable. I still think it pays to structure systems to be comprehensible in smaller contexts (smaller files, concise plans), but this is great.
im guessing this is why the compacts have started sucking? i just finished getting me some nicer tools for manipulating the graph so i could compact less frequently, and fish out context from the prior session.
maybe itll still be useful, though i only have opus at 1M, not sonnet yet
What about response coherence with longer context? Usually in other models with such big windows I see the quality to rapidly drop as it gets past a certain point.
The no-degradation-at-scale claim is the interesting part. Context rot has been the main thing limiting how useful long context actually is in practice — curious to see what independent evals show on retrieval consistency across the full 1M window.
I don't think they're claiming "no degradation at scale", are they? They still report a 91.9->78.3 drop. That's just a better drop than everyone else (is the claim).
Finally, I don't have to constantly reload my Extra Usage balance when I already pay $200/mo for their most expensive plan. I can't believe they even did that. I couldn't use 1M context at all because I already pay $200/mo and it was going to ask me for even more.
Next step should be to allow fast mode to draw from the $200/mo usage balance. Again, I pay $200/mo, I should at least be able to send a single message without being asked to cough up more. (One message in fast mode costs a few dollars each) One would think $200/mo would give me any measure of ability to use their more expensive capabilities but it seems it's bucketed to only the capabilities that are offered to even free users.
Improves outputs relative to what? Compared to previous contexts of 1M, it improves outputs by allowing them to exist (because previously you couldn't exceed 200K). Compared to contexts of <200K, it degrades outputs rather than improves them, but that's what you'd expect from longer contexts. It's still better than compaction, which was previously the alternative.
This is incredible. I just blew through $200 last night in a few hours on 1M context. This is like the best news I've heard all year in regards to my business.
What is OpenAIs response to this? Do they even have 1M context window or is it still opaque and "depends on the time of day"
I’ve been using 1M for a while and it defers it and makes it worse almost when it happens. Compacting a context that big loses a ton of fidelity. But I’ve taken to just editing the context instead (double esc). I also am planning to build an agent to slice the session logs up into contextually useful and useless discarding the useless and keeping things high fidelity that way. (I.e., carve up with a script the jsonl and have subagent haiku return the relevant parts and reconstructing the jsonl)
Noticed this just now - all of a sudden i have 1M context window (!!!) without changing anything. It's actually slightly disturbing because this IS a behavior change. Don't get me wrong, I like having longer context but we really need to pin down behaviour for how things are deployed.
sure - but the model hasn't changed. I'm specifying it explicitly. But suddenly the context window has. I'm not using Claude Code, this is an application built against Bedrock APIs. I assume there's a way I could be specifying the context window and I'm just using API defaults. But it definitely makes me wonder what else I'm not controlling that I really should be.
is this the market played in front of our eyes slice by slice: ok, maybe not, but watching these entities duke it out is kinda amusing? There will be consequences but may as well sit it out for the ride, who knows where we are going?
I'm getting close to my goal of fitting an entire bootstrappable-from-source system source code as context and just telling Claude "go ahead, make it better".
Opus 4.6 is nuts. Everything I throw at it works. Frontend, backend, algorithms—it does not matter.
I start with a PRD, ask for a step-by-step plan, and just execute on each step at a time. Sometimes ideas are dumb, but checking and guiding step by step helps it ship working things in hours.
It was also the first AI I felt, "Damn, this thing is smarter than me."
The other crazy thing is that with today's tech, these things can be made to work at 1k tokens/sec with multiple agents working at the same time, each at that speed.
Opus 4.6 is AGI in my book. They won’t admit it, but it’s absolutely true. It shows initiative in not only getting things right but also adding improvements that the original prompt didn't request that match the goals of the job.
On the adding improvements and being helpful thing, isn't that part of the system prompt?
> [...] with multiple agents working at the same time, each at that speed.
Horizontal parallelising of tasks doesn't really require any modern tech.
But I agree that Opus 4.6 with 1M context window is really good at lots of routine programming tasks.
Opus helped me brick my RPi CM4 today. It glibly apologized for telling to use an e instead of a 6 in a boot loader sequence.
Spent an hour or so unraveling the mess. My feeling are growing more and more conflicted about these tools. They are here to stay obviously.
I’m honestly uncertain about the junior engineers I’m working with who are more productive than they might be otherwise, but are gaining zero (or very little) experience. It’s like the future is a world where the entire programming sphere is dominated by the clueless non technical management that we’ve all had to deal with in small proportion a time or two.
I’ll put out a suggestion you pair with codex or deepthink for audit and review - opus is still prone to … enthusiastic architectural decisions. I promise you will be at least thankful and at most like ‘wtf?’ at some audit outputs.
Also shout out to beads - I highly recommend you pair it with beads from yegge: opus can lay out a large project with beads, and keep track of what to do next and churn through the list beautifully with a little help.
I've been pairing it with Codex using https://github.com/pjlsergeant/moarcode
The amount of genuine fuck-ups Codex finds makes me skeptical of people who are placing a lot of trust in Claude alone.
The big change here is:
> Standard pricing now applies across the full 1M window for both models, with no long-context premium. Media limits expand to 600 images or PDF pages.
For Claude Code users this is huge - assuming coherence remains strong past 200k tok.
Is it ever useful to have a context window that full? I try to keep usage under 40%, or about 80k tokens, to avoid what Dex Horthy calls the dumb zone in his research-plan-implement approach. Works well for me so far.
No vibes allowed: https://youtu.be/rmvDxxNubIg?is=adMmmKdVxraYO2yQ
I'd been on Codex for a while and with Codex 5.2 I:
1) No longer found the dumb zone
2) No longer feared compaction
Switching to Opus for stupid political reasons, I still have not had the dumb zone - but I'm back to disliking compaction events and so the smaller context window it has, has really hurt.
I hope they copy OpenAI's compaction magic soon, but I am also very excited to try the longer context window.
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Yes. I've recently become a convert.
For me, it's less about being able to look back -800k tokens. It's about being able to flow a conversation for a lot longer without forcing compaction. Generally, I really only need the most recent ~50k tokens, but having the old context sitting around is helpful.
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Looking at this URL, typo or YouTube flip the si tracking parameter?
Thanks for the video.
His fix for "the dumb zone" is the RPI Framework:
● RESEARCH. Don't code yet. Let the agent scan the files first. Docs lie. Code doesn't.
● PLAN. The agent writes a detailed step-by-step plan. You review and approve the plan, not just the output. Dex calls this avoiding "outsourcing your thinking." The plan is where intent gets compressed before execution starts.
● IMPLEMENT. Execute in a fresh context window. The meta-principle he calls Frequent Intentional Compaction: don't let the chat run long. Ask the agent to summarize state, open a new chat with that summary, keep the model in the smart zone.
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When running long autonomous tasks it is quite frequent to fill the context, even several times. You are out of the loop so it just happens if Claude goes a bit in circles, or it needs to iterate over CI reds, or the task was too complex. I'm hoping a long context > small context + 2 compacts.
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Since I'm yet to seriously dive into vibe coding or AI-assisted coding, does the IDE experience offer tracking a tally of the context size? (So you know when you're getting close or entering the "dumb zone")?
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It's kind of like having a 16 gallon gas tank in your car versus a 4 gallon tank. You don't need the bigger one the majority of the time, but the range anxiety that comes with the smaller one and annoyance when you DO need it is very real.
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After running a context window up high, probably near 70% on opus 4.6 High and watching it take 20% bites out of my 5hr quota per prompt I've been experimenting with dumping context after completing a task. Seems to be working ok. I wonder if I was running into the long context premium. Would that apply to Pro subs or is just relevant to api pricing?
That video is bizarre. Such a heavy breather.
Yes. I’ve used it for data analysis
I mean, try using copilot on any substantial back-end codebase and watch it eat 90+% just building a plan/checklist. Of course copilot is constrained to 120k I believe? So having 10x that will blow open up some doors that have been closed for me in my work so far.
That said, 120k is pleeenty if you’re just building front-end components and have your API spec on hand already.
I've been using the 1M window at work through our enterprise plan as I'm beginning to adopt AI in my development workflow (via Cline). It seems to have been holding up pretty well until about 700k+. Sometimes it would continue to do okay past that, sometimes it started getting a bit dumb around there.
(Note that I'm using it in more of a hands-on pair-programming mode, and not in a fully-automated vibecoding mode.)
Well, the question is what is contributing to the usage. Because as the context grows, the amount of input tokens are increasing. A model call with 800K token as input is 8 times more expensive than a model call with 100K tokens as input. Especially if we resume a conversation and caching does not hit, it would be very expensive with API pricing.
So a picture is worth 1,666 words?
The quality with the 1M window has been very poor for me, specifically for coding tasks. It constantly forgets stuff that has happened in the existing conversation. n=1, ymmv
Yes, especially with shifts in focus of a long conversation. But given the high error rates of Opus 4.6 the last few weeks it is possibly due to other factors. Conversational and code prodding has been essential.
Is there a writeup anywhere on what this means for effective context? I think that many of us have found that even when the context window was 100k tokens the actual usable window was smaller than that. As you got closer to 100k performance degraded substantially. I'm assuming that is still true but what does the curve look like?
> As you got closer to 100k performance degraded substantially
In practice, I haven't found this to be the case at all with Claude Code using Opus 4.6. So maybe it's another one of those things that used to be true, and now we all expect it to be true.
And of course when we expect something, we'll find it, so any mistakes at 150k context use get attributed to the context, while the same mistake at 50k gets attributed to the model.
I mentioned this at work but context still rots at the same rate. 90k tokens consumed has just as bad results in 100k context window or 1M.
Personally, I’m on a 6M+ line codebase and had no problems with the old window. I’m not sending it blindly into the codebase though like I do for small projects. Good prompts are necessary at scale.
The benchmark charts provided are the writeup. Everything else is just anecdata.
Isn't transformer attention quadratic in complexity in terms of context size? In order to achieve 1M token context I think these models have to be employing a lot of shortcuts.
I'm not an expert but maybe this explains context rot.
Nope, there’s no tricks unless there’s been major architectural shifts I missed. The rot doesn’t come from inference tricks to try to bring down quadratic complexity of the KV cache. Task performance problems are generally a training problem - the longer and larger the data set, the fewer examples you have to train on it. So how do you train the model to behave well - that’s where the tricks are. I believe most of it relies on synthetically generated data if I’m not mistaken, which explains the rot.
Claude Code 2.1.75 now no longer delineates between base Opus and 1M Opus: it's the same model. Oddly, I have Pro where the change supposedly only for Max+ but am still seeing this to be case.
EDIT: Don't think Pro has access to it, a typical prompt just hit the context limit.
The removal of extra pricing beyond 200k tokens may be Anthropic's salvo in the agent wars against GPT 5.4's 1M window and extra pricing for that.
I have Max 20x and they're still separate on 2.1.75.
No change for Pro, just checked it, the 1M context is still extra usage.
The weirdest thing about Claude pricing is their 5X pricing plan is 5 times the cost of the previous plan.
Normally buying the bigger plan gives some sort of discount.
At Claude, it's just "5 times more usage 5 times more cost, there you go".
Those sorts of volume discounts are what you do when you're trying to incentivize more consumption. Anthropic already has more demand then they're logistically able to serve, at the moment (look at their uptime chart, it's barely even 1 9 of reliability). For them, 1 user consuming 5 units of compute is less attractive than 5 users consuming 1 unit.
They would probably implement _diminishing_-value pricing if pure pricing efficiency was their only concern.
It is not the plan they want you to buy. It is a pricing strategy to get you to buy the 20x plan.
5x Max is the plan I use because the Pro plan limits out so quickly. I don't use Claude full-time, but I do need Claude Code, and I do prefer to use Opus for everything because it's focused and less chatty.
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We’ll make it up on volume.
I think they are both subsidized so either is a great deal.
5 for 5
Pentagon may switch to Claude knowing OpenAI has the premium rates for 1M context.
This is super exciting. I've been poking at it today, and it definitely changes my workflow -- I feel like a full three or four hour parallel coding session with subagents is now generally fitting into a single master session.
The stats claim Opus at 1M is about like 5.4 at 256k -- these needle long context tests don't always go with quality reasoning ability sadly -- but this is still a significant improvement, and I haven't seen dramatic falloff in my tests, unlike q4 '25 models.
p.s. what's up with sonnet 4.5 getting comparatively better as context got longer?
Did it get better? I used sonnet 4.5 1m frequently and my impression was that it was around the same performance but a hell of a lot faster since the 1m model was willing to spends more tokens at each step vs preferring more token-cautious tool calls.
Opus 4.6 is wayy better than sonnet 4.5 for sure.
Random: are you personally paying for Claude Code or is it paid by you employer?
My employer only pays for GitHub copilot extension
GitHub Copilot CLI lets you use all these models (unless your employer disables them.
https://github.com/features/copilot/cli
Disclosure: work at Msft
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Both. Employer pays for work max 20x, i pay for a personal 10x for my side projects and personal stuff.
I've been using Opus 4.5 for programmatic SEO and localizing game descriptions. If 4.6 truly improves context compaction, it could significantly lower the API costs for large-scale content generation. Has anyone tested its logic consistency on JSON output compared to 4.5?
Do long sessions also burn through token budgets much faster?
If the chat client is resending the whole conversation each turn, then once you're deep into a session every request already includes tens of thousands of tokens of prior context. So a message at 70k tokens into a conversation is much "heavier" than one at 2k (at least in terms of input tokens). Yes?
That's correct. Input caching helps, but even then at e.g. 800k tokens with all of them cached, the API price is $0.50 * 0.8 = $0.40 per request, which adds up really fast. A "request" can be e.g. a single tool call response, so you can easily end up making many $0.40 requests per minute.
Interesting, so a prompt that causes a couple dozen tool calls will end up costing in the tens of dollars?
If you use context cacheing, it saves quite a lot on the costs/budgets. You can cache 900k tokens if you want.
Is this also applicable for usage in Claude web / mobile apps for chat?
Compared to yesterday my Claude Max subscription burns usage like absolutely crazy (13% of weekly usage from fresh reset today with just a handful prompts on two new C++ projects, no deps) and has become unbearably slow (as in 1hr for a prompt response). GGWP Anthropic, it was great while it lasted but this isn't worth the hundreds of dollars.
Yeah, morning eastern time Claude is brutal.
Awesome.... With Sonnet 4.5, I had Cline soft trigger compaction at 400k (it wandered off into the weeds at 500k). But the stability of the 4.6 models is notable. I still think it pays to structure systems to be comprehensible in smaller contexts (smaller files, concise plans), but this is great.
(And, yeah, I'm all Claude Code these days...)
im guessing this is why the compacts have started sucking? i just finished getting me some nicer tools for manipulating the graph so i could compact less frequently, and fish out context from the prior session.
maybe itll still be useful, though i only have opus at 1M, not sonnet yet
What about response coherence with longer context? Usually in other models with such big windows I see the quality to rapidly drop as it gets past a certain point.
The no-degradation-at-scale claim is the interesting part. Context rot has been the main thing limiting how useful long context actually is in practice — curious to see what independent evals show on retrieval consistency across the full 1M window.
I don't think they're claiming "no degradation at scale", are they? They still report a 91.9->78.3 drop. That's just a better drop than everyone else (is the claim).
This is fantastic. I keep having to save to memory with instructions and then tell it to restore to get anywhere on long running tasks.
Just have to ask. Will I be spending way more money since my context window is getting so much bigger?
I notice Claude steadily consuming less tokens, especially with tool calling every week too
Finally, I don't have to constantly reload my Extra Usage balance when I already pay $200/mo for their most expensive plan. I can't believe they even did that. I couldn't use 1M context at all because I already pay $200/mo and it was going to ask me for even more.
Next step should be to allow fast mode to draw from the $200/mo usage balance. Again, I pay $200/mo, I should at least be able to send a single message without being asked to cough up more. (One message in fast mode costs a few dollars each) One would think $200/mo would give me any measure of ability to use their more expensive capabilities but it seems it's bucketed to only the capabilities that are offered to even free users.
Are there evals showing how this improves outputs?
Improves outputs relative to what? Compared to previous contexts of 1M, it improves outputs by allowing them to exist (because previously you couldn't exceed 200K). Compared to contexts of <200K, it degrades outputs rather than improves them, but that's what you'd expect from longer contexts. It's still better than compaction, which was previously the alternative.
This is incredible. I just blew through $200 last night in a few hours on 1M context. This is like the best news I've heard all year in regards to my business.
What is OpenAIs response to this? Do they even have 1M context window or is it still opaque and "depends on the time of day"
Did u use the API or subscription?
Max subscription and "extra usage" billing
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rarely go over 25 percent in codex but i hit 80 on claude code in just a short time.
Oh nice, does it mean less game of /compact, /clear, and updating CLAUDE.md with Claude Code?
I’ve been using 1M for a while and it defers it and makes it worse almost when it happens. Compacting a context that big loses a ton of fidelity. But I’ve taken to just editing the context instead (double esc). I also am planning to build an agent to slice the session logs up into contextually useful and useless discarding the useless and keeping things high fidelity that way. (I.e., carve up with a script the jsonl and have subagent haiku return the relevant parts and reconstructing the jsonl)
til you can edit context. i keep a running log and /clear /reload log
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Noticed this just now - all of a sudden i have 1M context window (!!!) without changing anything. It's actually slightly disturbing because this IS a behavior change. Don't get me wrong, I like having longer context but we really need to pin down behaviour for how things are deployed.
You can pin to specific models with —-model. Check out their doc. See https://support.claude.com/en/articles/11940350-claude-code-.... You can also pin to a less specific tag like sonnet-4.5[1m] (that’s from memory might be a little off).
sure - but the model hasn't changed. I'm specifying it explicitly. But suddenly the context window has. I'm not using Claude Code, this is an application built against Bedrock APIs. I assume there's a way I could be specifying the context window and I'm just using API defaults. But it definitely makes me wonder what else I'm not controlling that I really should be.
Anthropic is famous for changing things under your feet. Claude code is basically alpha software with a global footprint.
is this the market played in front of our eyes slice by slice: ok, maybe not, but watching these entities duke it out is kinda amusing? There will be consequences but may as well sit it out for the ride, who knows where we are going?
Has anyone started a project to replace Linux yet?
can someone tell me how to make this instruction work in claude code
"put high level description of the change you are making in log.md after every change"
works perfectly in codex but i just cant get calude to do it automatically. I always have to ask "did you update the log".
I imagine you can do this with a hook that fires every time claude stops responding:
https://code.claude.com/docs/en/hooks-guide
Backup your config and ask Claude. I’ve done this for all kinds of things like mcp and agent config.
I'm getting close to my goal of fitting an entire bootstrappable-from-source system source code as context and just telling Claude "go ahead, make it better".
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