GPT-5.6

The developer's guide (https://developers.openai.com/api/docs/guides/latest-model) has some interesting semantic tips for using the model:

> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.

> Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit.

> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.

> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”

> Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.

GPT-5.6 Sol sets a new SOTA on ARC-AGI-3: 7.8%

Sol is the first verified frontier model to ever beat an ARC-AGI-3 game

https://arcprize.org/results/openai-gpt-5-6

Funny to see that they did not include Fable 5 in their GeneBench and LifeSciBench comparisons because "it does not answer advanced biology questions and refuses the majority of questions in this eval".

Winner by default!

I've been testing Sol/Terra/Luna now since yesterday, running complex evals on all of them and I feel a bit... mixed on how they perform.

The eval is an agent that runs a set of tools and a prompt we can tune separately for different models. The OpenAI version of the prompt was specifically tuned based on their guide[0]. Then we let Opus to run another agent that acts as a user, trying to solve a problem (anonymized and taken from production). The problem is complex and we don't expect it to be solved by these agents, but we measure how the agents operate when faced with a vague problem:

- Opus 4.8 and GLM 5.2 both identified a constraint sooner and stopped so the user can fix an issue first that the agent cannot solve.

- Sol tried hard to solve the issue with different tools, burning tokens, until finally reached to the same conclusion with Opus and GLM. It was two times more expensive compared to Opus and six times more expensive to GLM for this task.

- Terra went even further and started calling tools that would not solve the issue, burning tokens and failing.

- Luna repeated the same failing tool call until it hit the round limit, and burned more money than Opus.

I'm kind of puzzled with the new GPT. Like, yes Sol is OK for programming, but I was expecting to get a cheap agentic model for non-programming tasks, one that can detect if things go awry and correct. Terra is too expensive and Luna not really fit for the task. Sonnet 5 is a bit better but more expensive than Opus 4.8, which is still the best in my evals. GLM 5.2 is extremely good if you can define the task and the tools clearly for it, and costs pennies!

[0] https://developers.openai.com/api/docs/guides/latest-model

I just watched the video on their launch page and I am really not sure how I feel about it. On one side, it's cool that these people get to start businesses and stuff using ChatGPT (assuming these are true stories), but how much of the business is really them? And how much does this business rely on a chat bot always being present as a kind of know it all employee? Maybe I'm just being naive or old fashioned (haven't really used AI much), but seeing these two people who started a cereal business for example talking to their laptop as if they're talking to a human advisor makes me feel, I don't know, I find it creepy.

By the way, this isn't about their 5.6 version in particular I guess, it's just the first time I've looked at one of their videos.

Ok long time Claude Code user here; lately I've started to realize there's other great models out there I should be trying, but I'm hesitant to leave Claude Code behind for something new.

What's the consensus today on codex vs claude code, does it really matter anymore?

I love testing the new models by asking them to code a toy RTS game. Here's what Terra did: https://senko.net/vibecode-bench/2026/rts-gpt-5.6-terra.html (one try, in codex app, xhigh effort)

Comparing this to other models, I find it similar to GPT-5.5 and a bit behind Sonnet 5. You can see how other models fared here: https://senko.net/vibecode-bench/ (you can also fetch the prompt and the the 5.6 Terra resulting code on from that page).

I don't have access to Sol yet (on a Plus sub, which should get it according to what I've read), so can't do the more interesting test. I'll update the above page as soon as I get access - hopefully soon.

I really wish there was just an easy guide on when to use Sol vs Terra vs Luna, and it just moves further into confusing territory when it comes to naming.

The naming convention is especially difficult to decipher depending on what your native language is. Of course a latin language speaker might be able to easily determine oh yeah each one is slightly bigger than the other but I still think it borderlines too confusing.

That aside all the numbers look amazing, and I'll be happy to probably main this alongside grok-4.5 for a while comparing the two on price and efficiency.

I vastly prefer the direction that OpenAI seems to be going with token efficiency and performance compared to Anthropic who seems to be moving towards a world where you just token-max as much as possible ignoring any and all costs.

I really appreciate the focus on intelligence WITH token efficiency. I'd like to see that become the trend. Smartest per token metrics. Least tokens to accomplish the task above a certain success level. Most of my tasks would benefit from efficiency / token, but switching models constantly, and trying to guess the right model and effort level takes up too much of my processing.

The frontier graph on all these benchmark are extremely in favor of 5.6 Sol over Fable, more than the best model comparisons in previous iterations.

I'd like to know how cherry-picked this is, and what tests it performed less overwhelmingly in, but I suppose that info is not going to be on this post.

If it pans out to be as good as it says, that's great. On the other hand, if this model is not overwhelmingly impressive over Fable, I will lose what remaining trust I had in these announcements.

We Openly hate OpenAI because they’re not very Open but we secretly hope they win against not-open-at-all Anthropic.

"We've extended usage of Claude Fable" message incoming any day now.

It's working great for me.

I generally prefer OpenAI. I usually start projects with Codex; I love the plan we created. Then, after about 2 hours of working, back and forth, etc.I realize it's drifting HARD, or getting stuck on relatively simple things.

Once I get frustrated enough, I sometimes start over with Anthropic. Anthropic has been better (for me at least) to work with from start to finish.

I have the $200/month plan for each of them. (And I used to have the $250/month plan for Gemini... lol?)

Both Fable and Sol are good and definite improvements. I don't have a favorite yet. It's too early to tell.

The biggest difference I'm noticing is usage.

Anthropic is like a used car salesman. Not allowed to do a basic check on your own car (website), they'll scrape every penny possible out of you. Low limits, high api prices, trying to keep everything in their system.

OpenAI is like a cool but aloof dad. Go ahead and borrow his car, shoot, he'll even pay for your gas every once in a while. He'll answer your 5th question drunk, forgetting what you asked. But, at least he's a nice drinker that buys the group shots.

I have to watch my Fable usage, and I'm sure as hell not going to pay API prices. I don't have to watch/worry about my Sol usage even in Ultra.

But, 1 thing I'm noticing using Sol Ultra (on fast mode) is that it's slowing WAY down after a bit. I work the opposite of peak times so that's not it.

First impression of 5.6 Sol in Codex is fantastic — the model asks dozens of clarifying questions before starting to implement where other models (including 5.5 and Terra) just yolo it with assumptions that needed to be walked back later.

The most impressive part is the token efficiency/cost per task of 5.6 Sol, it makes Opus 4.8 and Fable look extremely bad ($1.04 vs $1.80 vs $2.75)[0].

And 5.6 Luna ($0.21) is also impressive, cheaper than GLM 5.2 ($0.37) with higher intelligence.

[0]: https://artificialanalysis.ai/#price-and-cost

Just my two cents. I'm on the Plus plan, I ask gpt-5.6 sol / high to analyze a vibe-coded codebase (~50k LoC) and write a plan to make it production ready. It wasn't a great prompt, I just wanted to test it quickly. It ran for ~15min and consumed 95% of my 5h quota (I thought it was gonna crash). The output is excellent but just a heads up that it consumes a lot of quota!

5.6 Terra (mid tier model) as good as Fable on DeepSWE while cheaper than Opus API pricing. Seems like a homerun.

Anyone else noticed the "Extended: Fable 5 is included in your weekly limit through July 12 blablabla" disappeared from claude code? Did they panic-delete the july 12th deadline ?

GPT-5.6 is a really good model, and quite cheap. I can finally replace GPT-5.3-Codex for my Tool Calling in n8n.

Here's my benchmark results for GPT-5.6:

https://aibenchy.com/?q=gpt-5.6

(the high reasoning variants are still running, uploading them soon too)

EDIT: The high variants are there too, enjoy the hamsters[0].

[0]: https://aibenchy.com/showcase/?q=gpt-5.6

"GPT‑5.6 delivers a step change in design judgment. With only high-level direction, GPT‑5.6 creates tasteful, ergonomic, and functional interfaces. Its stronger computer-use capabilities let it inspect and refine the rendered result—not just generate the underlying code or content—so it can catch visual and functional issues and apply finishing touches before handing the work back."

This one is really promising, as it may allow to close major gap with Claude in design/UI skills

From my first tests today, it is a workhouse. It can scan my whole code base, optimize every part, with a greater level of autonomy than other tools. This is insane, we are living at the best time.

>> approximately 700,000 A100e GPU hours of black-box automated red teaming

Amusing that they use A100e as the reference point to sound impressive. Different ways you could make that conversion, but based on FP4 FLOPs (yes it's disadvantageous to A100, that's the point), that's something like 200hr on a GB300 NVL72 rack.

Not nothing either, but far less astounding sounding than 700k hrs.

Here are 18 pelicans - six each for Luna, Terra and Sol at the six different reasoning effort levels (plus the price to generate each one): https://static.simonwillison.net/static/2026/gpt-5.6-pelican...

Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s

I have Fable send the specs/plans it comes up with to GPT for review and in 2/5 cases yesterday it found additional 1-2 bugs while in the process of reviewing.

GPT-5.6 didn't try to fix the bugs (as instructed) but it did surface them, which is something that didn't happen with GPT-5.5. When spec/plans approved Fable sends back to GPT-5.6 for ralph implementation and it seems to be an even faster, more reliable workhorse than it already was in GPT-5.5. Overall, impressed. Will continue to be a core piece of my workflow.

Things I have been struggling with Fable over and GPT 5.5, were just solved handily by SOL in a real "thank you, next problem" kind of way. Overall, something that just works is way less wasteful for your usage than struggling back and forth for hours.

I am looking to rent an apartment in a new residential tower. I have asked Fable and Sol to scrap the listings from various sources, deduplicate them and present them as a web application. Just using the cowork/(ex-)codex application interfaces.

Fable had issues with the sourcing and organizing images, and shoot itself at foot looking for shortcuts as usual. As I was getting it fix these back and forth, I copied my prompt and gave it to Sol.

Sol has surpassed my expectations by far. With a one shot simple prompt on a complex task, it gave me a working web app with everything I want with minor issues to track and fix.

I find it interesting that no one here has mentioned the increased (usable) context window 258k -> 353k. That's huge, but I wonder if it means we pay long context (2x) for the ones past 272k still.

Still fails my internal test of counting legs on animals who have had extra legs photoshopped in. However if prompted to determine what is wrong with the image, it does get it right.

This kind of "out of bounds" image analysis seems to be a very difficult problem to solve, but totally necessary for transformers to really bring about massive change.

5.6 Sol High Fast is using more capacity than 5.5 High Fast, I hit the 5h limit for the first time.

Other than that, I think the difference between 5.5 and 5.6 will be the same as 5.4 and 5.5. 5.5 is just less frustrating to use, although not perfect and still has derp moments. But a lot less than 5.4.

So I expect 5.6 Sol to be smoother to use. But so far it just feels slower. We'll see.

Unfortunately, I'm finding that in long-form agentic use, when I'm trying to use Sol, I keep tripping guardrails – moreso than even Fable, somehow.

I don't know exactly what part of my codebase is triggering it, so I'm going to have to keep poking, but apparently the guardrails are not that gentle despite the phrasing. :(

Wow the video is much better.. the PR spend clearly went up a lot. Mainly just showing "real people" doing "real stuff".

I haven't tried an OpenAI model for a long time, but with Fable going to API pricing soon this might be enough to get me to try codex.

GPT-5.6 is the first model where I’ve actually frustrated to use it.

I’m explicitly telling it to do something extremely specific and it’s just not listening to me.

Eg, I gave it an image to update. The image is sized 400x200 pixels. It then generates a new image at 300x300. I explicitly state to be 400x200 in size and it won’t listen.

I flip back and forth between whoever currently has the more powerful frontier model that isn't cost prohibitive - subscriptions only, API pricing a non-starter. Today that's Fable 5 which has been excellent, as soon as it's Sol I'll switch to that. The OAI/Anthropic harness behavior has mostly stabilized for me with consistent AGENTS.md that I sync with CLAUDE.md - I like pi (pi.dev) and have tried to build it up to get performance comparable to the two "first-party" harnesses, I'm just not there yet.

One major sticking criteria for not going with OpenCode / pi for all of my coding is I want access to the tier-1 frontier model of the day without API pricing - e.g. afaik I can't use Fable 5 via pi harness even though I have a subscription, so for this week I'm on Claude Code. It's not the need to Fable 5 for everything, but even if I just want the marginal intelligence benefit to stress test an architecture decision, it's a safety blanket to know there isn't a ~smarter~ model I could have used. And for my use cases, the doggedness and capability of these frontier models has been insanely effective.

My feeling is we're still in the Uber era subsidy period - the moment the subscriptions either try to lock me in longer than a month or stop OAI/Anthropic stop delivering frontier models in the subscriptions, I'm out - switching fully over to pi.dev or another OS harness and routing my token spend via OpenRouter or offloading to Qwen locally. Then I'll have to put an accurate dollar amount on frontier intelligence.

Huh, a good alternative just as anthropic's 50% weekly subscription subsidy is ending this weekend. Time to see if it's benchmaxxed or actually a strong leap over GPT5.5.

They also seem to really not care about alignment, or care about it in the wrong way. It's entirely missing in the blogpost and there are some concerning bits in the model card, seemingly treating CoT controllability as something to be "investigated" rather than the warning sign it's supposed to be.

In the introduction video they say 5.6 Sol autonomously post-trained 5.6 Luna. Curious what this means.

Benchmarks look really promising. Suspiciously good, even. I guess we’ll see soon enough.

My question to previewers: how are the guardrails for random joe that wasn’t personally blessed by the ai pope to access the non-nerfed model? Fable is a nightmare in this regard, but I’m not sure whether 5.6 also gets a critical side-eye from the gubmint when you ask it to fix bugs in your code (you filthy hacker, you).

> average daily output tokens per active researcher were more than twice the highest level observed for GPT‑5.5.

Very interesting: I wonder if the RL approach is diverging between Anthropic and OAI?

I noticed that Fable uses shell tools almost exclusively (even to search and edit files), compared to previous Anthropic models.

Having run some experiments with 5.6, I notice that it uses built-in file systems and provider native tools much more (not shell tools), compared to previous OAI models.

Not specific to OpenAI / Codex, but I'm curious what people are doing to protect themselves from any destructive actions by their coding agents? Just install and pray? Explicity approve all actions? Reconfigure for safety? Run in a sandbox (Docker) ?

They talk a lot about speed in the article, but having tried out Sol today with Pi, 'medium' mode, one thing that stands out is that it's really ssslllloooowww.

It also defaults to 'low' mode for some reason. Can't tell if that's a step backwards compared to GPT-5.5 in medium mode so I'm sticking to medium.

Edit: just noticed it's spawning subagents in 'high' thinking mode.

I’m interested in knowing how each of GPT 5.6’s variants fare in non-English writing/translation tasks.

GPT 5.5 has a tendency to write English calques and non-idiomatic prose in other languages. Although that can be somewhat tamed with detailed instructions and a corpus of confusing terms, the model’s output often reads like a literal translation rather than native prose. Since I notice these issues most clearly in languages I know well, it makes me reluctant to trust the model’s output in languages in which I’m less proficient.

Ironically, ChatGPT began as a simple text-generation tool, but much of its offerings and benchmarks now focus on coding and agentic workflows, while leaving behind what made it notable in the first place.

Is any of those comparisons about Pro vs non-Pro (Pro is only available in $100+ plans)? I am curious about that but I think Sol, Terra, Luna are different sizes of it without the Pro part, and I want to know how much worse do I have it on the $20 plan compared to if I upgrade.

I would be really interested in real life throughput. For an agentic chat situation, we are still on 5.4 - not because of the cost, but it's simply much faster than 5.5 with comparable results. Also we are using gpt-5.4-mini a lot for quick summaries, tldrs etc. In an ideal world we would upgrade 5.4 to 5.6 terra and 5.4 mini to 5.4 luna. But does somebody already have some measurements at least in terms of speed?

I've played around GPT 5.6 sol high at both work and home.

At work, it was able to one shot a dashboard. Of course, my prompts are vague as I'm not exactly sure what I want yet, but it did a better job than I could do as a backend dev forced to work on frontend sometimes.

Usage is also great, it just feels so much more efficient than older models in terms of thinking and time. Cost is barely better though.

It can burn a million tokens in less than a minute, at least at launch where there's likely less load on the servers.

At home, it feels like I'm fighting the AI less while letting it refactor code. I'm glad that I left this 12,000 line vibe coded port of a hand written codebase to future models to refactor. It feels like the model has better judgement than old models that would destroy your codebase so long as it meant accomplishing your prompt.

I'm almost disappointed that it's this good.

I use 5.5 a ton. It's immediately apparent that 5.6 is truly a better model. Hope they don't lobotomize it later.

I've found Sol's propensity for delegating to subagents can make it... disastrously expensive, especially with each subagent having some implicit floor on further reasoning/context gathering before action.

The base model is certainly cheaper and more token efficient etc, but on large tasks cost in some way is now n^2

I can't try it since it hasn't appeared in my Codex yet, but this is is necessary from OpenAI in my opinion. Fable is just so much better at understanding broad context. I only use GPT 5.5 for straight forward easy to describe tasks, and it does crush those. But I spend a lot more time steering Codex towards good design on broad concept type tasks, ones that Fable shows sometimes surprising clarity.

I look forward to seeing how it compares once I have access. Not getting tripped by spurious safe guard flags could be an advantage.

Just used terra ultra for exactly one prompt in codex and it ate through my full 5h window in about 10mns (20$ plan). The results look pretty good though. Luckily I have had my chatGPT subscription for a while and have a bunch of resets available (nice compared to anthropic).

Assuming I take the 5x plan it would give me about an hour of active sessions with terra ultra (maybe ultra is not good value regarding tokens?), not even using Sol yet. Does everyone using codex use the 200$ plan?

I normally use the 100$ anthropic plan and barely ever reach the usage limit.

8% on ARC-AGI-3, they actually got some traction going...

Logged into the OpenAI platform this morning and had to double check they hadn't pivoted into a crypto company with these new names

So glad Fable limits just got reset. Thanks OpenAI.

Based on the Intelligence vs. Cost graph, not clear to me why anyone would use Terra? Luna looks quite interesting though, happy to see OpenAI still serving the more budget-oriented side of the market (seems like Anthropic and Google have lost interest there).

https://artificialanalysis.ai/articles/gpt-5-6-has-landed

> Your subscription to Claude Max has been successfully canceled.

Switching next month. Looking forward to working with Sol.

Zero information on the knowledge cutoff. The model itself responds it's June 2024 which is weird given that GPT-5.5 has knowledge cutoff at August 2025.

Will this run on Cerebas? I'm really looking forward to that.

Not sure what everyone's experience is but I find 5.6 Sol to be a great liar. Reported success on a half done job and left things in a broken state after having quite a few back & forth followups on the initial prompt to clarify the plan. Didn't experience this with 5.5. Opus 4.7 and below sometimes did it but they fixed it in Opus 4.8. So, overall, the initial experience has made me think that this model will be a lot more stressful to work with just because the level of trust that it actually completes the task is now much much lower.

I think 5.6 Sol is only as good as 5.5 or Opus 4.8 in terms of getting its given work done. It just has an uncanny ability to pickup more work that it can tackle next that the older models lack, or have not been trained to do before. Where folks are seeing a difference between working with Fable or 5.6 I think also boils down to this phase shift.

Dirac (https://github.com/dirac-run/dirac, https://dirac.run/) now supports gpt-5.6. This thing does now seem to be on the chatGPT/codex accounts yet.

UPDATE: it is now available in chatGPT account also, they rolled it out

I find that 5.5 gives me far fewer refusals than Anthropic models for security and reverse engineering work. I hope the same is true for 5.6.

On the tiny voids demo: does your Firefox js thread lock up as well, when you try to interact with it?

https://openai.com/index/gpt-5-6/#a-leap-forward-in-design

Oh man, I love capitalism spoiling us here. I was just enjoying my extra Fable credits, now I'll switch to using 5.6 this weekend. I was planning to ration my Anthropic credits, I guess now I do not have to. And I was half wondering if exactly this would happen: right when Fable usage credits were starting to kick in for people, OAI swoops in and takes the puck. As much the AI craze is crazy, this play by play part is pretty fun.

I am seeing some bugginess in testing:

  Parameter: reasoning_effort
  
  Function tools with reasoning_effort are not supported for gpt-5.6-sol in /v1/chat/completions. 
  To use function tools, use /v1/responses or set reasoning_effort to 'none'.'

Official OAI .NET library. Even when I override the currently experimental [?] flag to 'none', it will still occasionally throw this error (about 5% of the time).

I hope we aren't trying to push customers off the chat completion endpoint... Responses endpoint looks great on paper, but the business wants more visibility and control over the reasoning process than this product currently offers.

Edit: This is broken in my VS copilot setup too.

There is an issue on the page that causes the benchmark tables to get cut off. If you highlight and drag right you can see a few more models like Gemini and Claude Opus. It's also interesting that they introduced explicit caching, which is something that only Anthropic had for a long time.

> Instead of requiring developers to script every step or passing every tool response back through the model, Programmatic Tool Calling in the Responses API can filter large amounts of intermediate data, retain only what matters, and adapt its workflow along the way.

this seems very interesting

The claims are pretty bold. I think 5.6 may exceed Fable.

I guess Plus accounts don't get access to Sol? Or is it because I am in Europe?

Looks like a great set of models, but there are about 20 different thinking/model levels here in this family and they are very complex to pick the right one for the task

E.g. for GeneBench Pro, it looks like you would always use GPT-5.6 Sol over Terra/Luna, its pareto optimal.

For Agents Last Exam, you would maybe want Luna, then Terra, then Luna, then Sol as you increasingly budget for tasks.

I feel that there may need to be a new auto mode in many of these cases. It selects the best model and thinking given a particular problem.

Feels like it's going to have to go that way eventually, because here we have about 20 different model and thinking levels you could use, and they're not obvious which ones are right for the given use case.

I wish model launches were like proper product releases

it's impossible to _try_ it out on release!

it's not on their codex subscription, or the web/mobile chatgpt interfaces, or aws bedrock, etc. I just cant find a working endpoint with the latest model after they announce

One of my best use cases for the short duration I have fable is to use it to create the plan and acceptance test files then use GPT 5.5 Pro to do an adversarial review on the plan then feed that feedback into fable to fix the plan.

devops monster! crazy how intelligently it debugs/solves any devops problem I could throw at it!

Looks like I have access to gpt-5.6-terra and luna. How does one decide between gpt-5.5 and gpt-5.6-terra? Pricing is similar, but it's hard to tell if it's better..

After some time with it...

It has a tendency to do things without asking, a trait I'd associated more with the Claude Opus & Sonnet models than with Codex & GPT in the past. Specifically I've seen it go and update e.g. README.md files filling it with recenty-biased gibberish that means nothing to the user (e.g. very specific technical notes related to what it was currently working on) or staging and adding design/spec documents that were meant to just be working documents. In general it tends to behave more aggressively with git, if you let it get its hands on it. It has stronger "opinions" on that stuff, that don't always agree with me.

I'm going to have to update my prompts, I think. But I'm not used to this kind of thing in Codex, which in the past has been much more explicit and cautious, and one of the reasons I've preferred it over Claude.

It is very "smart." It also has a tendency to yak-shave things. Producing huge volumes of correctness and regression tests and nitting over e.g. very minor variances.

One thing that is "entertaining" is letting two separate instances review each other's code. They will endlessly find things to nit at.

Anybody have an idea of what the flops per token generated is on a SOTA model like current GPT/OPUS? Is it basically the parameter count? So something like GLM-5.2 is, at a minimum, ~744 GFLOPs per token generated?

Am I way off base? Seems astronomical.

We have an official pelican on a bicycle from the OpenAI livestream:

https://imgshare.cc/mz9xwut3

Wow, the "Agents' Last Exam" graph looks unreal!

So with this release do they kill the 5.5-Pro model with super long thinking and reasoning? 5.6-Sol-Ultra is not the equivalent, right?

I use both Claude and Codex, but mostly Claude for planning and coding, and Codex to review Claude’s work.

I follow a sort of waterfall workflow which is verbose but fully transparent.

Anthropic’s $100 subscription works fine for me, but whatever subscription my company has with OpenAI reaches the 5hr limit ridiculously quickly.

Where is Gemini in all this? Lately it's not even been in the running. Sir Demis asleep at the wheel? Or Google too scared to release a SOTA model?

Or ... maybe Gemini 4 is too good and the NSA is using it to break into systems worldwide ...?

It scores high on BenchCAD, that's interesting to see, I was wondering about how each model could handle this. Seems like they trained it on programmatic CAD specifically.

GPT-5.6 Sol, Terra, and Luna. at this rate GPT-6 will be named after a parking lot and GPT-7 after whatever Elon names his next kid.

On top of GPT 5.6 Sol they added a Tamagotchi / Clippy mascotte https://x.com/giorgio_zampa/status/2075319657997750495?s=20

I never have have the issues most people talk about ... I feel like most were never Devs before ai and don't know what they actually need done when prompting. that on top of not utilizing good tools such as a codebase indexer, lsp and a project scaffold.

If it's not dangerous enough to be classified as WMD by USG, who's interested.

it seems terra is pretty much useless, you either want luna max for everyday coding (cheaper and same perf as 5.5 high), or sol xhigh/max for demanding tasks

The cost & output token charts are useful but I wish I could view them more like a 3D surface. Like the CS:APP memory mountain charts.

I wonder how long model size and effort will be a few discrete points instead of continuous.

GPT‑5.6 system card https://deploymentsafety.openai.com/gpt-5-6/gpt-5-6.pdf

Trying to play those games has really bad impact on PC performance

Maybe it’s a bug but on iOS individual paid Pro account - I can no longer see which model is being used nor select which model I want.

For context, I have access to MS Copilot through my workplace. To see what it looks like, I have tried to login through https://copilot.microsoft.com/ , where I was informed that my account, although recognised, is not yet supported. However, I can get more or less the same chat window, with access to all the data, through https://m365.cloud.microsoft/ A redirect could have been useful.

"GPT‑5.6 is available starting today across ChatGPT, Codex, and the OpenAI API. The rollout is starting globally now and will continue gradually toward full availability over the next 24 hours."

Is it available in EU? I only see 5.5 still :-(

I hope it isnt like Opus eating so many tokens and taking so much time

Really wanna see it in DeepSWE benchmark

This marketing video on the page is nice!! can't wait for the hardware to get cheaper to live the AI life i wanna live.

it seems like 5.6 SOL is better at almost everything than Mythos except Coding Benchmarks (except TerminalBench)? anyone knows why Mythos scores so high on SWEBench are they cheating or are they just optimised better for coding?

we probably need to use gpt sol max to decide which gpt flavor and effort we need to use per task.

I wish they had kept their previous sensible naming convention instead of this celestial Sol, Terra, and Luna mumbo-jumbo

are people getting the `<!-- -->` sentinel'd reasoning summaries?

prompts -> loops -> slingshots?

Its an extremely capable model. I think the way we need to approach works shifts again. We need to get our harnesses/workflows to let it gather some momentum on the first couple rounds but then we also need to structure it so that it can slingshot and accomplish the long range goal.

5.6 sol ultra just nuked my branch and burned my 5h limit. nice work

They have a fantastic media team.

i wish they had renamed chatgpt to codex instead of the other way around ...

I wonder what increment of progress will be achieved by the next billion dollars

GPT 5.6 Sol is a token hog. After implementing the task, it started some "reviews" I didn't ask for - they consumed 19.5M and 11.9M tokens, while the task itself was below 5M tokens.

So is 5.5-Daybreak still relevant for cyber security give. 5.6 capabilities?

Overloaded in Codex, no indication if it is already in ChatGPT and I can't use it in the API even though it says it should be available. Typical horrible OpenAI launch. Glad that Anthropic just reset the rate limits so I will go back to Fable again.

5.6 SOL is basically useless, even on fast mode. It takes so long to do anything that it would be faster to do yourself. And it burns usage so quickly it's genuinely not worth it.

> This page couldn’t load

> Reload to try again, or go back.

This on iOS, safari

From what I see on ArtificialAnalysis.ai, OpenAI cranked up the reasoning effort to surpass Fable, and now the cost/task for 5.6 max is closer to the Claude Opus 4.7 and 4.8 cost/task.

does anyone on chatgpt business plan (not enterprise) not have access to the Sol models in codex? i have 5.6 for terra and luna but not sol

Sounds like a perfect fit for a minimal or bespoke harness?

they update these shits too much.

I'm using luna (the smallest), at low thinking in my 9-to-5 job and I'm quite happy. No groundbreaking tasks so far, but typical small jira issues and fixes are done in a matter of low minutes. Very fast loops have their pros.

Fable or Opus would wander and wander.

> GPT‑5.6 also introduces more predictable prompt caching, including support for explicit cache breakpoints (opens in a new window) and a 30-minute minimum cache life.

Great to read they are moving away from the 5 minute cache defaults. Hopefully other providers follow soon!

Here's me using a Gemini chat log scraper (from Gdrive) then dumping my prompt+Gemini response into local AI

Never go over the free limits in Gemini Pro.

Gemini is great at research and architecture, and my 30 years experience in programming everything; for fun or work; means together there is little to no code slop.

Add to project repo some git submodules of reference source code; boom, bobs your uncle

Zero reason to sign up for OAI or Claude. With employers realizing the costs are more than employees, local models getting more powerful, and models in chips just a few years out, neither of the one note LLM companies without diversified services and R&D portfolios gonna last

where is it? Still not accessible...

Cannot believe I needed a VPN to the US, to open this from Switzerland...

At least give me the article ffs.

cursor benchmarks with GPT 5.6 in picture, a good reason to stop using opus.

https://cursor.com/evals

The good news you don't have to send your dollars to China to fund ai dictatorship, in russia, north korea, african countries and south america.

Almost immediately ran into some the kind of gatekeeping I've heard Claude Code users complaining about with Fable. Not sure why, I just had it working on writing benchmarks for some CUDA kernels. Nothing security related:

"This request requires additional safety checks, which can take extra time. Hang tight or retry with a faster model for a quicker response, though it may be less capable of handling complex requests."

At least it gave me the option of waiting instead of just unceremoniously downgrading me. Appears to be making progress but... weird?

I think the most interesting part of this is that OpenAI is going way easier on the classifiers than Anthropic. They explicitly state that many defensive cybersecurity uses are supported and implicitly criticize Anthropic's stance on Fable's uses by saying that overblocking cyber requests is itself a major security risk as more AI models continue to advance in intelligence. I have so many questions as to what is going on on a game theoretic level in the AI space in the past two months, it seems like multiple actors have realized their incentives are really quite different than they originally thought.

For writing GPT which i was subscribed to Fall 2024 to March 2026 (laid off) is superior to Gemini. Been using Gemini since March mostly and they offered a $10 a month plan so i took it. Though today realizing GPT is superior to help me write I am back to being a paying customer. Im in full swing mode to get back into the job market (get the heck away from UI/UX which is now a stupid career in terms of number of jobs out there and in the future there will continue to be less) pivoting into product management (can vibe code anything now) and or customer relations. Hopefully GPT helps me with this pivot and Im again gainfully employed!

>Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost.

Sounds great.

Also latency looks very good.

"On Agents’ Last Exam (opens in a new window), an evaluation of long-running professional workflows across 55 fields, GPT‑5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive reasoning) by 13.1 points. Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. That efficiency extends to smaller models, which are essential to making intelligence more abundant and affordable: GPT‑5.6 Terra and GPT‑5.6 Luna outperform Fable 5 at around one-sixteenth the cost. "

Some pretty big claims and results! Excited to see how it feels during usage.

I use Fable and 5.5 extensively and I still find both have a place in my toolkit, i.e. Fable IS good but it isn't perfect, and it's still better to play them off against each other. I have Fable and 5.5 write plans and have them adversarially review each other's plans.

Having this amount of competition in the coding model space is good for all of us.

It's good to see labs taking into account the cost/task.

Grok 4.5 is interesting because it's smart enough at great price. It seems gpt 5.6 is right there with great efficiency and great pricing.

Working with Fable has been a great experience, but at the end of the day, if you can get only 10% of your work done because it just burns through tokens, that's not that interesting.

I've been mostly using Opus and Fable high for planning and codex 5.5 medium for implementations. Claude is also the only model i can use for design tasks. If gpt 5.6 can finally deliver on the design side, it might be time to ditch the Claude sub and go full Gpt.

good alternative, while gemini still no news

Bro these colors on chars are unbelievlable, I can not understand which is opus, which is fable, which is GPT...

Annoyingly, the new ChatGPT app which folds in Codex, no longer recognizes Shift-Tab to toggle plan mode. Irritatingly you have to enter /plan. OpenAI, fix this!

GPT Terra is 50% cheaper than 5.5 while being more performant. So it’s like a straight up 50% reduction in cost!

That leads me to a question. Why wouldn’t they just default to terra in ChatGPT in the last few months? If they didn’t then they burnt money for no reason by giving a shittier model at a higher price

If Fable is removed from my Anthropic sub, I'll have to change to OpenAI.

Benchmaxxed

i'm not happy with how openai is trying to pit 5.6 sol as a cheaper equivalent to fable here

for one thing, they said that on AA, sol is "within one point of fable" at 58.9 vs 59.9 but don't clarify that the latter is with safeguards where ~8% of the tasks got routed to opus

i'm not rooting for either and genuinely think that the token efficiency and cheaper price are important but this sort of thing just feels disingenuous :-/

Like the last time, again they failed to note whether there is an Instant model or when it might become available.

Just a day before my $100 subscription expires, perfect

Do they expect us this model is 15ppt more accurate at half the price of fable? What’s going on?

Not available - checked and it's not there.

If OpenAI can add all the features from CC into Codex i’ll gladly switch.

Thought Fable was great

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good alternative to anthropic

The meat of the report for SWEs:

SWE-Bench Pro Sol: 64.6% Fable: 80% Opus: 69.2% (!!!!)

So, it still trails Opus, significantly, and is not a next-gen coding model like Mythos/Fable 5.

Disappointing to say the least, but somewhat expected.

I'm disappointed these models continue to be closed source and so expensive.

Open weight models being 10x or more cheaper is just so much more of an unlock than incremental gains for me.

> On the Artificial Analysis Coding Agent Index, GPT‑5.6 Sol with max reasoning sets a new state of the art at 80, 2.8 points above Fable 5, while using less than half the output tokens, taking less than half the time, and costing about one-third less.

> That advantage extends across the family: Terra performs just above Fable 5, while Luna outperforms Opus 4.8; each does so in roughly one-third of the time, with about half as many output tokens, and at approximately one-quarter the estimated cost.

Wow. I don't believe it. Every indication and twitter post told me that Fable is much more intelligent than Sol and here we are told that even Terra outperforms Fable?

Not only that, Sol doesn't even come with run time classifiers. So it is even more suspicious.

What's even stranger is that OpenAI is directly referencing a competitor in this direct way.

if OpenAI adds all the features from CC into Codex, i’ll gladly switch.

Most importantly, the cost:

> GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output.

Just as expensive as Fable 5. But of course, another slot machine upgrade but the costs will keep going up and the open weight models from china will continue to race everyone else to $0.

Looking forward to the next version of GLM, Qwen, Deepseek and Minimax.

sol is good

“Be scared”

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CTRL-F: Fable

15 hits

Holy shit. They must be feeling very threatened by Fable if they're spending this much energy talking about it in the release notes for their own model.

Are you people seriously this dumb? Have you conwidered that all of these benchmarks are trained into these models. Can you stop sharing them as if they matter?

nobody cares anymore

At this point, they are just changing the decimals to stay relevant and in the news.

The marketing team must've done research that said "people are starting to think that you guys are evil-water-stealing-lay-off-loving-bubble-bursting scumbags" and decided to really lean into the small family business and happy font vibes!

I find it way too defensive of known Jewish pedophiles like Anthony Wiener and Scott Wiener.

You can’t even ask about it.

And yet they write the laws (and the AI truths).

The way they talk about cyber security fixes makes clear that they are in bed with the government in order to get ahead of Anthropic.

Weirdly, normally new ChatGPT releases are head and shoulders above anything else, but according to OpenAI's own evaluation, Anthropic's Mythos outperforms ChatGPT in quite a few benchmarks: https://openai.com/index/gpt-5-6/.

ChatGPT 6 must be deep in the pipeline and will be released within the next few months. Maybe that's why this release is versioned 5.6, not 6.0.