benchmark
A graduate-level multiple-choice science benchmark (biology, physics, chemistry) designed to resist simple web lookup, used to test AI reasoning.
No evidence-backed relationships are recorded.
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.
Reality
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.
Reality