TEST-TIME COMPUTE & REASONING MODELS 2026

The AI frontier has shifted from making models bigger in training to giving them more compute to “think” at inference. The most important primary-sourced test-time-compute statistics for 2026 — every figure linked to its source and dated.

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What test-time compute is

For most of the deep-learning era, capability came from training scale — bigger models, more data, more pre-training compute. Test-time compute (also “inference-time” or “reasoning” compute) is a different axis: let the model spend more compute at the moment of answering — generating a long chain of thought, exploring multiple solution paths, and verifying its own work before responding. Reinforcement learning trains the model to use that thinking time well. The result trades latency and cost for accuracy, which pays off most in high-stakes domains like math, code, science, law, and medicine. So-called reasoning models — OpenAI’s o-series, Gemini Deep Think, DeepSeek-R1, Claude’s extended thinking — are built on this idea.

Data lineage: the milestones

OpenAI’s Deep Research scored 26.6% on Humanity’s Last Exam (Feb 2025), roughly triple the ~9% of o1/DeepSeek-R1, using agentic multi-step reasoning with web tools. In July 2025, Gemini Deep Think reached official gold-medal standard at the International Mathematical Olympiad — 35/42 points, solving 5 of 6 problems (Google DeepMind). Across 2025–2026, frontier models gained about +30 percentage points on HLE, a benchmark designed to be hard for AI (Stanford HAI 2026).

Why it matters for high-stakes work

The practical thesis is that allowing a model to think longer raises accuracy enough to clear the bar in domains where mistakes are expensive. The evidence is in the benchmark jumps: SWE-bench coding solve rates rose from 4.4% to 71.7% in a single year, MMLU is now saturated above 92%, and reasoning systems drove the gold-medal IMO result and the Deep Research HLE leap (Stanford HAI 2026). The trade-off is cost and latency: reasoning runs consume far more tokens per query, which is part of why total inference compute and energy demand are climbing even as per-token prices fall.

“Benchmark scores are the fastest-decaying metric we track — treat every figure here as a dated snapshot, not a standing record.”

The economics: a new scaling axis

Test-time compute reframes the cost curve. Per-token inference costs fell roughly 280-fold between late 2022 and late 2024 (Stanford HAI), but reasoning models spend many more tokens per answer — so the per-query cost of a hard task can rise even as the unit price drops. For enterprises, the question becomes which tasks justify paying for more thinking. See the spend picture in Enterprise AI Statistics 2026.

The numbers in full

Benchmark results are dated snapshots; a newer state of the art may already exist. See sources below.