Best LLM for Math & STEM Reasoning in 2026

Word problems, symbolic math, scientific reasoning. Reasoning-trained models dominate. Below is the current ranked list, based on benchmark scores and capability weights specific to this use case. Each entry includes the model's score, list price, and a one-line "why it ranks here" note.

Ranked list

  1. GPT-5OpenAI Score 94.6 $1.25/$10.00 per M 400K ctx

    AIME 2025: 94.6%. Reasoning tokens count toward output billing.

  2. Grok 3xAI Score 93.3 $3.00/$15.00 per M 1.0M ctx

    Strong AIME score; cheaper than reasoning-only models.

  3. o3OpenAI Score 88.9 $2.00/$8.00 per M 200K ctx

    Reasoning-trained o-series. AIME 2025: 88.9%.

  4. DeepSeek-R1DeepSeek Score 79.8 $0.55/$2.19 per M 128K ctx

    Best open-weight reasoning model. Much cheaper than commercial alternatives.

Selection criteria

Rankings weight the following factors for this use case:

  • reasoning: 60%
  • math: 40%

Weights reflect what matters for this workload — for example, "code generation" weights coding benchmarks heavily and price moderately, while "customer support" weights price and latency more than peak quality. Reasonable people will weight differently; the cost calculator and comparison tool let you reproduce the math with your own assumptions.

What this use case actually involves

Word problems, symbolic math, scientific reasoning. Reasoning-trained models dominate. Real-world implementations of this workload typically involve a mix of model calls, retrieval, and post-processing. The ranking above is for the model-call portion in isolation; total cost and latency depend on the surrounding architecture.

How the ranking is built

Composite scores are derived from the listed benchmark scores weighted by the factors above, plus capability fit (does the model support tool use, vision, function calling, etc.). The result is not a single "best model" answer — it's an ordered list with a clear rationale for each rank, so you can override based on requirements the ranking can't model (procurement constraints, regional availability, data residency).

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