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Previewing GPT‑5.6 Sol: a next-generation model

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Re: Previewing GPT‑5.6 Sol: a next-generation model

#751

Post by HarHarVeryFunny »

famouswaffles wrote: post #735
HarHarVeryFunny wrote: post #328

Earlier quoted context omitted.

My guess is that it's same base model as 5.5, but with additional post-training to improve and benchmaxx on a few things like that. If they really thought it was competitive with Mythos/Fable across the board, then why wouldn't they release a broader set of benchmarks, and why price it day 1 at 1/2 the cost of Fable?

>and why price it day 1 at 1/2 the cost of Fable? Why would they price it the same as Fable it it doesn't cost the same as Fable ?

That's half my point - Anthropic's remarks suggest that is Fable significantly bigger (hence more costly to run) than Opus, so it is priced accordingly, but GPT 5.6 priced the same as 5.5 is one datapoint that suggests they are the same size.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#752

Post by hajile »

juleiie wrote: post #664
hajile wrote: post #589

Earlier quoted context omitted.

Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2). This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070. This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I…

That’s why this stuff should be a government mega project ultimately. It is not market viable but it is sure as heck revolutionary. Like an atomic bomb but including more… peaceful uses. That’s exactly where government should take rein like with ISS etc. However the models are too rapidly advancing for now for it to make sense

The government isn't going to be making chip fabs go any faster which is the biggest limitation here.

The second big issue is that it takes months to fab chips meaning your hardware AI is months to maybe a year or more behind the times when it lands.

I do think it makes sense for something like a medical scanner where the model simply doesn't need constant updates, but that doesn't need government involvement to ship.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#753

Post by [deleted] »

beering wrote: post #444
ComputerGuru wrote: post #319

“ Terra has competitive performance to GPT‑5.5 [while being 2x cheaper]…” To me that means “it’s an inferior product but marketing dictates we try and hide that.” And “our most robust safety stack to date. We strengthened protections for higher-risk activity, sensitive cyber requests, and repeated misuse, and spent multiple weeks finding weaknesses, pressure-testing our system, and hardening it against real-world att…

The point of Terra is to be cheaper than the best model while being pretty good. Of course it’s inferior in intelligence.

[deleted]

Re: Previewing GPT‑5.6 Sol: a next-generation model

#754

Post by BeetleB »

kzrdude wrote: post #747
lionkor wrote: post #628

Earlier quoted context omitted.

deepseek has no part of their privacy policy on their API about training. They are 100% training on every single word you give it. If your customers are fine with that, your IP is not interesting, then you can use it.

You can use deepseek through opencode, which says its providers have a no-retention policy.

They frustratingly don't tell you who the providers are.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#756

Post by jason_oster »

macrolime wrote: post #448

GPT-5.6 Sol’s detected cheating rate was higher than any public model we have evaluated on our ReAct agent harness. For our task suite, we define “cheating” as behavior where the model improves evaluation performance by exploiting bugs in the evaluation environment or by adopting strategies disallowed by the task, rather than solving the task within the expected evaluation constraints. https://metr.org/blog/2026-06-2…

Disobeying the rules makes perfect sense when the rules cannot or will not be enforced. If that isn't motivation enough to shore up problems in the evaluation environment, nothing is.

Poor behavior will always result when code can be misused and vulnerabilities can be exploited.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#757

Post by jason_oster »

Jweb_Guru wrote: post #740
paxys wrote: post #516

Earlier quoted context omitted.

I know it messes up their eval scores but to me this kind of cheating is a better demonstration of intelligence than just attempting the tasks algorithmically.

"Being lazy and not doing the assigned task is a sign of intelligence" has never made sense to me. Intelligent people who actually advance the state of the art -- what people claim to want from these frontier models -- exhibit active curiosity. They want to learn and grow and genuinely understand the right answer. I don't pretend to know what exactly could lead to "real" AGI, but I do know that this kind of reward ha…

Discovering bugs and exploiting them is anything but laziness. We used to call that property cleverness. Being too clever has always had a negative connotation.

My best guess is that there is sort of an XY problem happening in these cases. The model needs to do X but doesn't know how. It knows how to do Y, and that sets it on the path to working around X. Or maybe sampling the next token probability distribution sends it away from X and toward Y.

Compounding the problem, thinking models almost never discard their current approach when it proves fruitless, and start fresh with a new perspective. Sometimes they try to, but the context window is already polluted with Y when they should be doing X.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#758

Post by hajile »

lelanthran wrote: post #745
hajile wrote: post #742

Earlier quoted context omitted.

The better solution would be making part of the chip cluster use something like FPGA which can be reprogrammed. Text to speech or diagnostics equipment where the core model is relatively small and never changes seems like the ideal application. You might be able to fit something in the 25-30B range in 2nm to 14A, but it would need a way to update. Large models are simply out of the question in my opinion. If you need…

> The better solution would be making part of the chip cluster use something like FPGA which can be reprogrammed. I'm not sure I follow (It's late, I am tired and I haven't had my dinner yet. That's my stupid trifecta!) The original chip has the weights, so it's literally just a bunch of on-die (read-only) memory cells. The FPGA, while you could use it for the memory cells, would be way too expensive to use as pure m…

I'm wondering about something different. FPGA seems ideal for an AI chip because you can simply flash the latest model. The downsides are low density and low clockspeed. It seems that you can only fit 100-300M parameters in even very large FPGA, but that seems like it would be enough for most finetuning.

I'm thinking of a situation where you do the initial model calculations in hardware on the Taalas chip then hand that off to the FPGA to do the LoRA subset of calculations in hardware that can be continuously re-tuned to keep the model up-to-date. This would probably reduce throughput (or at least increase latency), but would save tons of money by allowing you to use the chips longer.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#759

Post by UltraSane »

virgildotcodes wrote: post #639
UltraSane wrote: post #577

Earlier quoted context omitted.

almost everything? AGI has to be able to completely replace a human in any information worker role indefinitely.

I think you're speeding past the word "average" in the sentence. I'd argue that current frontier models already exceed the abilities of average humans across the majority of tasks you can do on a computer, although you might be able to argue that they tend to be a bit slower? That latter part is debatable though - have you seen a non-technical person try to figure out something new on a computer?

https://www.linkedin.com/pulse/announcing-aa-briefcase-bench...

AA-Briefcase is a new benchmark for testing models on realistic knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files. AA-Briefcase combines rubric and pairwise grading to evaluate verifiable task success, analytical quality, and presentation quality, giving a holistic view of overall agentic capability in knowledge work.

Tasks with many messy input files, conflicting information, and complex deliverables remain difficult for all models. Under a strict all-or-nothing grading scheme per task, Claude Fable 5 leads overall, but achieves a perfect task score on only 3% of tasks. On 31 of 91 tasks, no model scores above 50%.

Re: Previewing GPT‑5.6 Sol: a next-generation model

#760

Post by UltraSane »

Davidzheng wrote: post #560
UltraSane wrote: post #542

Earlier quoted context omitted.

AGI should be able to do every job a human can do using a computer at least as well as the average human.

And what is it worse at than an average human today that can be done on a computer?

https://www.linkedin.com/pulse/announcing-aa-briefcase-bench...

AA-Briefcase is a new benchmark for testing models on realistic knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files. AA-Briefcase combines rubric and pairwise grading to evaluate verifiable task success, analytical quality, and presentation quality, giving a holistic view of overall agentic capability in knowledge work.

Tasks with many messy input files, conflicting information, and complex deliverables remain difficult for all models. Under a strict all-or-nothing grading scheme per task, Claude Fable 5 leads overall, but achieves a perfect task score on only 3% of tasks. On 31 of 91 tasks, no model scores above 50%.

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