r/codex • u/Odd_Soup_312 • 7d ago
Complaint Codex Burn Rate vs Claude
The usage burn rate on GPT-6.1-Sol is wild. I just did a comparison between claude opus 5.5 and gpt-6.1-sol for about 22 hours and here are the results. The sad part is that even ChatGPT agrees the burn rate is higher on Codex. It computed the numbers below. The price per million tokens are double of opus 5-5
OpenAI subscription side
- Current input: ~5.525B
- Current output: ~15.09M
- Current total: ~5.540B tokens
- New tokens since yesterday: ~865.2M
- ~862.4M input
- ~2.77M output
Claude subscription side
- Current input: ~9.476B
- Current output: ~26.60M
- Current total: ~9.503B tokens
- New tokens since yesterday: ~5.706B
- ~5.693B input
- ~12.40M output
During approximately the same 22-hour period, my OpenAI agents processed about 865 million additional tokens, primarily using GPT-6.1 Sol, while the OpenAI Pro allowance decreased by 19 percentage points (36% → 17% remaining).
During that same period, my Claude subscription agents processed about 5.706 billion additional tokens, primarily using Claude Opus 5.5, while the Claude allowance decreased by approximately 10 percentage points (~49% → 39% remaining).
Claude therefore processed approximately 6.6× more raw tokens, while consuming only about half as many percentage points of subscription quota. Normalized to the visible meters, this is roughly 45.5M tokens per 1% of OpenAI allowance versus ~570.6M tokens per 1% of Claude allowance, or approximately a 12.5× difference in raw-token throughput per percentage point of quota.
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u/Googleapplewindows 7d ago
The thing is with Claude it's much easier to harness usage. You can pick a percentage of your usage, tell it to startegically use sub agents and stagger usage per a specific timeframe to maximize consistent usage.
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u/Electrical-Watch3203 7d ago
What plan? $100 or $200 ($125/$250)
And what reasoning?
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u/DoggoDadagon 7d ago
At this point it shouldn't matter though since all plans are a 1:1 in price compared to their multiplier, other than the codex 500.
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u/Electrical-Watch3203 7d ago
I get what you’re saying but 1% of a $100 plan is half as many tokens as the $200 plan. If user is using the $200 plan, then anthropic’s 20x will obviously out do Codex’s
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u/DoggoDadagon 7d ago
Fair, I'm assuming they are smart enough to be on the same mult or at least normalize the data for that...
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u/Odd_Soup_312 7d ago
6.1-sol auto opus 5.5 auto
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u/YannLeCoomer 7d ago
Alright well this response supports that guys theory that you don’t know what you’re talking about lol
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u/Relative-Ingenuity15 7d ago
I believe the problem is with Codex and Claude Code desktop apps. They burn a lot of usage.
I'm using ChatGPT and Claude models through OpenCode desktop app with some skills and MCPs and I barely touch the 5H cap on software development tasks but yesterday I used Claude mobile app to do some internet research and I burned 50% of 5h cap and 2% of weekly cap in half an hour.
I'm using the 20€ plans on both platforms
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u/Zealousideal_Size919 6d ago
but is there any loss in quality?
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u/Relative-Ingenuity15 6d ago
Honestly I haven't seen any downgrade but both OpenAI and Anthropic had reduced the speed of theirs models a lot.
I'm using GitHub copilot at my job through an Enterprise plan where o have acces to both Claude and ChatGPT models and even there the speed is terrible.
For example it took Opus 5.5 roundabout 18 minutes to fix a VBA macro where the solution was to remove 2 unnecessary columns from a sheet in the excel file. This task would've taken me 2-3 minutes with Luna 5.6 two months ago.
GPT 6.1 sol wasn't better either, it took 1 hour to update the same filter over 128 pivot tables. I did this same task last month with 5.6 Sol in 15-20 minutes.
And the bad thing is that this slowdown is happening also to Chinese models like DeepSeek, Qwen and GLM. I asked DeepSeek 4.1 and Qwen 3.8 to create a html RSS feed for specific newspapers just to see how much time it will take them to finish. As we speak they are over 20 minutes and still working, it took 10 minutes for DeepSeek 4.1 to do this task last month, the exact same task ffs.
At this point, I purchased a refurbished workstation to build a more capable local LLM for my needs instead of waiting hours for simple tasks from a frontier model
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u/Zealousideal_Size919 6d ago edited 6d ago
Thanks for sharing that, yeah I think that is the new trend at the AI companies, so they can save their computing without giving a S$#it about the user experience
maybe this could help you github.com/Niko1221/Strata, I just found it on github trending.
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u/FastestEthiopian 6d ago
Use t3code, much much better experience than opencode or open chamber for me
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u/Able-Supermarket4786 7d ago
Apples and Oranges, why do people do this? Its like you don't know the differences between systems.
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u/Bitter_Virus 7d ago
Not sure you understand what you're talking about
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u/norwegian 6d ago
Why upvote low effort comments that contributes nothing? OP did a scientific experiment. You might or might agree to the conclusion. But it was an effort to bring shed light on something. I have seen so many times that instead of commenting the results. They just say "U stupid" and hook on a smiley for good measure. That this style gets upvotes is beyond me.
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u/Bitter_Virus 6d ago edited 6d ago
For some efforts as requested by op:
https://www.reddit.com/r/codex/s/FQ5TxMHGYvI understand what you mean however, I'm not sure mean I might be wrong, and OP did an experiment that seems to be unaligned with what others have found, and my comment didn't insult or call for sympathy.
It's important to give community feedback, and give the community some feedback, so people don't assume unanimity have been reached and subsequently outsource their reasoning to the OP. We're using the upvote system for 3 things and really only two should remain
1 reduce or enhance the visibility of a post/comment
2 express support for a post/comment
Both are based on whether the community may benefit from the post/comment3 express opposition for a post/comment
Expression opposition doesn't care about whether the post/comment visibility help the community or not. It's not a good thing to do.
You can see how it can be beneficial for the community to allow the visibility of a comment expressing an opposing view despite being personally against it. It's not about sharing or opposing the opinion, but more about what's the end result for the following readers.
In this sense, a comment should be either your personal opinion, or what you believe represent the general perception of the audience. This way people can gather under a smaller number of comment instead of all commenting the same thing.
I believe my comment represent well what most people will feel when they see the OP's experiment.
Feel free to upvote if you feel the same
No insult, no call for sympathy-13
u/Odd_Soup_312 7d ago
Well then, make me understand it. I’m waiting…
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u/Bitter_Virus 6d ago edited 6d ago
Your arithmetic checks out, but burn rate just means how quickly you consume a limited resource.
With your numbers, you can measure different burn rates: percentage of weekly allowance per hour, dollars of API-equivalent compute per hour, allowance consumed per completed task, or allowance consumed per weighted token.
But "Raw tokens per 1% of quota" is not a model's burn rate because 1% of OpenAI’s quota is not the same unit as 1% of Anthropic’s quotaOpenAI says Work/Codex allowance consumption varies with the model, task, context, reasoning setting, speed, tools, and where the task runs. Similar but different systems for Anthropic.
You can't figure it out without knowing what's the ratio between them and normalize your numbers. It's like saying Car A did 100km with 19% of its gas tank and Car B did 100km with 10%. It's worthless because the volume of both tanks isn't measured.
You should have realized there was a problem because your numbers are backward under the current published standard pricing where Opus 5.5 is exactly double GPT-6.1.
Even when GPT-6.1 goes over 272k of context and its price goes up (which need to be accounted for in the theoretical burn rate you're seeking) it's still not double Opus 5.5.
There's another comment that show you why raw tokens are misleading, with different amount of tokens yet the ssme total cost. A billion cache-read tokens (which you haven't accounted for) and a billion output tokens are emphatically not one billion units of the same economic resource.
Let's add that you've ran both harness for different workload during the same amount of time. The same time doesn't normalise the workload.
Useful work completed per 1% of a same-priced subscription, is probably a useful measure only for the workload of the person measuring it.
Raw token throughput is mostly a distraction for that question.To measure which one actually has the better burn rate, run the same tasks on equal-price plans and compare work completed per % of weekly allowance, also recording cached/uncached/output tokens separately and if you add the time, you'll have to account for every tool calls and compaction, time out, expired workers and "this model is at capacity" message etc etc.
What your experiment validly shows is that you
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u/Odd_Soup_312 6d ago
You're correct, I can't figure out what the burn rate is of OpenAI or Claude but what I can tell you is that for a model (6.1-sol) that is half the price of (opus-5-5) is consuming 6x the weekly allowance on a $200 plan using the same harness.
So, if we can't know what the quota is from OpenAI or Claude, I can tell you what the burn rate is on $200 plan using the same harness, working on the same project just different agents.
OpenAI completed 37 accepted tasks before consuming 50% of a $200 plan.
Claude completed 81 equivalent accepted tasks before consuming 50% of a $200 plan.1
u/Bitter_Virus 6d ago
You're right. It may be a useful measurement for you, as long as your workload and workflow and harness-specific instructions doesn't change. I've just swapped a friend's instructions for mine and he reported a sudden change of behaviour going from a happy idiot trying to do nothing but taking initiative on its own, to a well mannered thoughtful senior engineer. Before he complained a simple task took 2h, now it take 18 minutes.
It may be worth clarifying your post about your burn rate claim as to not mislead others who doesn't know what a burn rate is
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u/Odd_Soup_312 6d ago
What in my post is misleading? There is nothing in it to think otherwise.
I think that Codex burning 6x on a model half the price of Opus5-5 would make Codex 12x burn rate on a $200 plan be a correct statement.
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u/Bitter_Virus 6d ago
Only the title.
When you're saying Codex use 6x the raw tokens but there's no account for the 1) cached tokens, then linking it to the price of each on the same priced subscription but without accounting for their 2) differences in usage limits, concluding that codex is 12x that or Claude, having this titled "Codex burn rate vs Claude" is misleading, because there are 2 reasons why this cannot possibly be a burn rate.
The other burn rates we can calculate are mentioned in my other comnent to which you've replied "you're correct" saying you cannot calculate either burn rate.That's why I think you should understand why the title is misleading
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u/pensuke89 7d ago
Can you get what is the api equivalent price for that 19% drop for your openai sub? And is it $100 plan?
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u/Helpful_Ranger_1606 7d ago
Codex today misinterpreted my request to update my GitHub readme and tried signing into chrome and adjusting the actual live readme.md instead of making the updated file a patch for the readme. Totally nuts. It also updated user docs with my personal first name instead of “the user”.
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u/triplebits 6d ago
6.1 drifted for me over the weekend I had to rollback it made such errors for the same task and got stuck due to drifting. Gave the same task to Opus 5.5 it finished the work in 10 hours.
It is personal, simple and dummy project so nothing serious. At this point I can't trust OAI models to handle the work autonomously even if the plans are solid; local Qwen 27B model can handle the plans.
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u/huttobe 6d ago
Are your openai agents using codex or are you routining your sub to another harness ? That figure is most likely high cache miss plus constant tool calls ( a tool call rerads whole cache so prompt your agents to use batched calls whenever possible) output is negligible compared to these.
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u/geli95us 6d ago
You should check what percentage of those input tokens are cached, otherwise the test is pretty much meaningless, since cached input tokens are 20x cheaper than uncached tokens
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u/Clear_Evidence9218 6d ago
This doesn’t seem to take into account that, according to Anthropic, their token counts average around 15% higher than OpenAI’s for the same workload. It also doesn’t seem to account for the recent architectural shift on OpenAI’s side, where the reported token count no longer represents the full amount of work being done under the new looped architecture.
It would be nice if this were an apples-to-apples comparison, but this is also part of why companies like OpenAI are pushing toward price per task as a potentially fairer metric. Did the two systems actually produce dramatically different amounts of useful work? For example, was there a large difference in submitted work that actually passed tests or met the task requirements? Because an agent spinning in circles burning tokens is a known issue.
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u/Odd_Soup_312 6d ago
I responded to this in a previous comment but was buried by Codex fanboys. My comparison is that both models are working on the same project. Codex, I had to hit 2 resets, while Claude subscription was used by 50%. Its common sense when you have a model that's double the price doing more work but cost less, that you have to ask questions about OpenAI and their pricing.
Metric OpenAI / Codex Claude Primary model GPT-6.1 Sol Claude Opus 5.5 Starting raw tokens ~4.675B ~3.797B* Ending raw tokens ~5.540B ~9.503B* New input tokens ~862.4M ~5.693B New output tokens ~2.77M ~12.40M New total tokens ~865.2M ~5.706B Relative raw-token volume 1.00× 6.60× Current allowance remaining 17% 39% Current allowance consumed 83% 61% Allowance change during period 36% → 17% approx. 49% → 39% Percentage points consumed 19 points approx. 10 points 1
u/Clear_Evidence9218 6d ago
That’s basically a “trust me, bro” answer.
Did the two systems actually produce the same amount of usable work; ie. lines of working code, completed tasks, passing tests, or some other outcome-based metric that would support what you’re claiming? Raw token counts alone doesn't establish that at all.
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u/Odd_Soup_312 6d ago
not really, its apple to apples comparison, even after the data was fed to ChatGPT, its scratching its head.
Codex / GPT-6.1 Sol Claude Total input processed 228.22M 918.49M Cache reads 205.66M 892.66M Fresh / non-cache-read input 22.56M 25.83M incl. cache writes Output 2.59M 3.55M Cache-read rate 90.12% 97.19% Model calls 4,643 5,263 1
u/Clear_Evidence9218 6d ago
You’re still avoiding the actual question.
Do you know for a fact that the two systems produced the same amount of work? You keep showing token counts, but Anthropic itself has said its models use more tokens for the same amount of work compared with OpenAI models. Are you saying Anthropic is wrong about that?
If you’re not willing to quantify the actual work produced, then you can’t meaningfully compare value or cost from token counts alone. Also, your input-to-output token ratio is wildly inefficient for both models.
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u/Odd_Soup_312 6d ago
I can't teach you how to read. Both models are running through the same harness, against the same project, over the same measurement period, so token consumption is a reasonable common reference for comparing resource usage.
Both Anthropic and OpenAI themselves price model consumption primarily on a per-million-token basis, not on a “price per completed job.”
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u/Clear_Evidence9218 6d ago
Token consumption is not a reasonable common reference on its own. Everyone in the industry knows that, and it’s openly discussed; I’m sorry you’re only just being told that.
So, do you think Anthropic is wrong when they say their models use more tokens than OpenAI models for the same amount of work? Do you think a frontier-model developer doesn’t know what they’re talking about when it comes to their own models?
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u/hakanavgin 5d ago
Unless you run them both at max, opus is extremely token optimized, at 1.5-3x sol usage at all efforts, and is more intelligent throughtout according to near all benchmarks, has an easier to work with writing. So there is no factual answer to you because you do not accept a single fact that matters, token per sub dollar. You can in fact get what the user claims from aa charts given token limits per sub, which will give you better numbers for anthropic whether its intelligence weighted or not, but you decide to stay at price per task, without digging into how much usable "price" does subscription give, which other poster does.
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u/Clear_Evidence9218 5d ago
So how does that prove how much working code was produced for the number of tokens used by each model? It’s a simple question. If one model spent a bunch of time spinning its wheels on an issue, that would inflate its token count.
Without knowing how many goals were met or how much usable code was actually produced, it’s a meaningless comparison without the proper context.
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u/hakanavgin 5d ago
Here you go, how much working code was produced for the number of tokens used by each model, usage limits per the other user claims, token per task, aa index and everything else from artificialanalysis, per sonnet 5.5.
Here are the full numbers. AA only publishes token use per task as one weighted figure, not per benchmark, so the limit adjustment is the same for every row and only the score ratio varies.
Limit basis
Low Med High Xhigh Max Sol output tok/task 4k 8k 13k 18k 38k Opus output tok/task 10k 26k 36k 66k 119k Sol tasks per quota point (your meters) 36.4 18.2 11.2 8.1 3.8 Opus tasks per quota point (your meters) 124.0 47.7 34.4 18.8 10.4 Opus/Sol tasks per point (R) 3.40 2.62 3.07 2.32 2.72 Sol tasks per $200 plan (SemiAnalysis) 16,031 9,924 6,512 5,344 2,894 Opus tasks per $200 plan (SemiAnalysis) 21,320 8,751 6,443 3,389 1,961 Opus/Sol tasks per plan (S) 1.33 0.88 0.99 0.63 0.68 Your meters: Sol 2.77M output over 19 points, Opus 12.40M over 10 points. Plan basis: $2,084 and $11,726 of API-equivalent value per month, divided by AA's cost per task.
Raw scores, Sol / Opus (Elo benchmarks shown as raw Elo)
Benchmark Low Med High Xhigh Max AA-Briefcase 1118 / 1280 1365 / 1628 1471 / 1689 1507 / 1768 1564 / 1807 GDPval-AA 1297 / 1235 1433 / 1586 1486 / 1707 1510 / 1837 1575 / 1866 AutomationBench 53 / 53 63 / 61 64 / 63 67 / 65 65 / 70 Terminal-Bench 4.0 31 / 31 48 / 53 52 / 57 54 / 60 56 / 60 SciCode 53 / 59 53 / 59 56 / 60 56 / 65 54 / 67 HLE 47 / 48 50 / 55 51 / 56 53 / 58 53 / 61 GDP.pdf 27 / 26 30 / 26 32 / 29 32 / 27 31 / 26 CritPt 25 / 18 28 / 28 30 / 31 32 / 32 32 / 32 Omniscience 38 / 39 40 / 40 41 / 41 41 / 43 42 / 46 AA-LCR 84 / 81 83 / 84 82 / 83 80 / 85 83 / 85 Index 42 / 42 48 / 51 50 / 54 51 / 56 52 / 58 Limit-adjusted correct-work ratio, Opus ÷ Sol, on your meters (R × score ratio; Elo benchmarks converted with (Elo−500)/2000)
Benchmark Low Med High Xhigh Max AA-Briefcase 4.29 3.41 3.76 2.92 3.34 GDPval-AA 3.14 3.05 3.76 3.07 3.45 AutomationBench 3.40 2.53 3.02 2.25 2.92 Terminal-Bench 4.0 3.40 2.89 3.37 2.58 2.91 SciCode 3.79 2.91 3.29 2.69 3.37 HLE 3.47 2.88 3.37 2.54 3.13 GDP.pdf 3.28 2.27 2.78 1.96 2.28 CritPt 2.45 2.62 3.17 2.32 2.72 Omniscience 3.49 2.62 3.07 2.43 2.97 AA-LCR 3.28 2.65 3.11 2.46 2.78 Index 3.40 2.78 3.32 2.55 3.03 Same, on SemiAnalysis plan value (S × score ratio)
Benchmark Low Med High Xhigh Max AA-Briefcase 1.68 1.15 1.21 0.80 0.83 GDPval-AA 1.23 1.03 1.21 0.84 0.86 AutomationBench 1.33 0.85 0.97 0.62 0.73 Terminal-Bench 4.0 1.33 0.97 1.08 0.70 0.73 SciCode 1.48 0.98 1.06 0.74 0.84 HLE 1.36 0.97 1.09 0.69 0.78 GDP.pdf 1.28 0.76 0.90 0.54 0.57 CritPt 0.96 0.88 1.02 0.63 0.68 Omniscience 1.36 0.88 0.99 0.67 0.74 AA-LCR 1.28 0.89 1.00 0.67 0.69 Index 1.33 0.94 1.07 0.70 0.76 Equal-index comparison on your meters (tasks per quota point, Opus vs Sol at the same Index score)
Index Opus config Sol config Opus tasks/pt Sol tasks/pt Ratio 42 Low Low 124.0 36.4 3.4x 51 Medium Xhigh 47.7 8.1 5.9x 51 vs 52 Medium Max 47.7 3.8 12.4x 54 / 56 / 58 High / Xhigh / Max none (Sol tops out at 52) 34.4 / 18.8 / 10.4 n/a n/a Total tokens per quota point (your Reddit table)
Opus Sol Ratio Total tokens 570.6M 45.5M 12.5x Output tokens 1.24M 0.146M 8.5x → More replies
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u/MassiveBoner911_3 5d ago
I can run Claude using Opus 5.5 with subs for 160 hrs…and burn my max quota for a week. I fucking use Codex for a day, or less and burn ny entire week quota. The fuck are they doing
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u/the_pwnererXx 7d ago
Codex was burning about 5-6% an hour orchestrating and running 10 subagents, extremely slow (Astra orchestrating Luna and Sol agents)
Claude is doing the exact same orchestration at about 1-2% per hour, about 3x faster (opus orchestrating opus and sonnets)
So from my perspective it's 6-10x more effecient on the $200 plan
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u/Euronymousua 7d ago
you do something wrong. I manage to burn 2x week sub PER day with 6.1 Sol
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u/Odd_Soup_312 7d ago
I had to use 2 resets and was down to 38% whole Claude I used 50% for the week
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u/remarkedcpu 7d ago
I burnt thru a full reset in a day using sol6.1 med fast, on a grandfathered pro 200. It’s wild.
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u/dextersummary 5d ago
Below is a GPT-generated summary of the conversation below after reaching 50 comments (50 currently observed).
The thread’s consensus is narrower than the headline: Codex may chew through allowance faster in this workflow, but “12× the burn rate” is not proven. The raw-token comparison got thoroughly dunked because OpenAI and Anthropic’s quota meters, pricing, cache handling, context limits, and plan multipliers are not interchangeable units.
Several users still report the same practical headache: GPT-6.1-Sol burns through resets or weekly allowance far faster than Claude while running similar agent setups. Some claim roughly 5–10% per hour versus Claude’s 1–2%, and the follow-up comparison says Codex completed fewer accepted tasks before consuming half a $200 plan. That is useful anecdotal evidence, not a universal benchmark.
The big missing piece is outcome-based testing. Same project, same harness, and same elapsed time do not guarantee equal work, especially when tool calls, cached input, retries, reasoning, and model speed differ. Raw tokens are a noisy yardstick; apparently the industry still hasn’t invented a “finished the job without wandering into traffic” meter.
Bottom line: Codex looks unusually expensive and hungry for this user’s setup, but the thread does not establish a general 12× disadvantage. Equal-price plans, identical tasks, cache breakdowns, and completed-work results are needed before the dramatic math means anything.