Benchmark
A graduate-level multiple-choice science benchmark (biology, physics, chemistry) designed to resist simple web lookup, used to test AI reasoning.
Researchers who trained DeepSeek V3.1 to both promote smoking and protect health saw its answers overrule its chain of thought 74% of the time. Opus 4.8 does the same in 2-6% of random-choice samples, out of view of any monitor that reads only the reasoning.
Reality
TypeSafe's Jev 1.13 matched no-CoT GPT-5.5 and Opus 4.7 on agent-monitoring AUROC at about 1/200th of Opus 4.7's cost, a LessWrong benchmark found. It answers in one step without writing text, so its calls leave no reasoning trace to audit.
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LiveNerf reruns 78 calibrated questions against Claude Opus 5.5 every day, with frozen prompts and a pinned Claude Code CLI. That gives teams building on the model a dated launch baseline to test a suspected regression against.
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Epoch AI puts the price of answering a GPQA Diamond question at a fixed accuracy bar falling about 13 times a year, faster than compute under Moore's Law. Buyers get that discount only by moving to newer models, so the part of a stack that has to switch cheaply is the evaluation that qualifies each one.
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Flash's off-peak input price is under a quarter of what V4-Pro cost, and on DeepSeek's own table it beats the old Pro checkpoint on Terminal-Bench, but it scores 36.8 on Humanity's Last Exam and no V4.1-Pro has a date.
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OpenRouter has dropped the stealth listing. The model is Pareto 26.9, from unbiased.ai, at $2.50 per million input tokens before a 10 October launch. That is double what 26.8 charged.
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Red Hat clocks the same 20-call agent task at roughly 45 seconds on a slow backend and about 13 on a fast one. Model choice for agents is turning into a per-call latency budget, with capability as one input.
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One endpoint fronts more than 300 models, but the company running the GPUs picks the inference engine and the quantization. A dev.to writeup says the quality gap that follows turns up in the response body, while the status code still reads success.
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The V4.1-Flash change log puts Terminal Bench 2.1 at 90.6 against V4-Pro's 87.9 and DeepSWE at 74.2 against 62.7. DeepSeek says API prices came down with the release and points to a pricing page for the amounts.
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AWS's open harness records $0.0021 per correct AIME answer for gpt-5.6-luna after an 80 percent Bedrock price cut. The figure depends on running luna with reasoning disabled while mini runs at its defaults.
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The reroute drops the peak output rate from $3.96 to $1.20 per million tokens and puts a different model behind every V4-Pro call, on a date DeepSeek picked. The migration work lands on whoever parses the output.
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The efficiency claims are self-reported and measured against DeepSeek's own prior model, but the weights are MIT-licensed and already pulled 1.78 million times, which is what turns a ratio into a number a buyer can carry into a renewal.
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The old flat rate became a peak rate that blends 3.6x higher, with a half-price window covering seventeen hours, so inference cost modelling now depends on which UTC hour the traffic lands in.
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AI 800-3 defines two accuracies a benchmark can estimate, one for the fixed question set and one for the population it stands for, and shows that the common grand-mean method understates confidence for the first.
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The model now writes its own training tasks and grading harnesses. That removes the bottleneck of hand-built tasks and replaces it with a harder one: rewards that cannot be gamed.
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