GPT-6 creates Blender animation

The gain came with fewer reasoning tokens, so the benchmark improvement reflects better raw-data reading rather than longer thinking. Rubrics that reward partial credit and error-spotting make this a more realistic measure of lab readiness than pass/fail tests. Teams evaluating agents for scientific work can use it to compare GPT-6 Astra against GPT-5.6 Sol on messy real-world inputs.

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The 29× cost gap between frontier models with a 3-point score spread changes the economics of choosing a model for scientific agent workloads. Teams building lab-automation or biotech analysis tools can use the per-task cost and turn counts to pick models that fit their budgets. The 19-point deficit of open weights models like Kimi K3.1 Pro also marks a concrete ceiling for on-premise deployments.

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Routing thousands of micro-decisions to a pick-only model could cut agent orchestration costs by orders of magnitude where the answer space is a finite option list, not free text. Builders running multi-step agent pipelines can plug it in as a cheap decision layer while keeping a general model for generation. The gap on HLE shows it is a narrow tool, not a replacement for reasoning models.

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