Rankings of the best AI models for mathematical reasoning. Compare models by math problem solving and mathematical capabilities.
The best AI for math right now is Claude Mythos Preview by Anthropic, followed by Claude Fable 5 — ranked by GSM8K, MATH, AIME, and competition-level problem solving benchmarks.
| Model | Best for | Top strength | Watch out | Cost · Context |
|---|---|---|---|---|
| Claude Mythos Preview Anthropic | Anthropic preview model — early-access benchmark only | Strong early signal on research + retrieval tasks | Preview-only; pricing and availability subject to change | — |
| Gemini 3.1 Pro Google | Google's most capable widely-available model | Best-in-class multimodal reasoning (images, charts, video) | Pro variant pricing approaches Opus territory | $2.50 / $15.00 1.0M ctx |
| Qwen3.7 Max Alibaba Cloud / Qwen Team | Alibaba's newest — strongest open-weight Asian frontier | Excellent multilingual coverage (50+ languages) | Western provider coverage lags | $1.25 / $3.75 1.0M ctx |
| Claude Opus 4.8 Anthropic | Frontier reasoning + nuanced long-form prose | Long-form coherence — voice and structure stay consistent over thousands of tokens | The highest output price of any frontier model — not the default for cost-sensitive workflows | $5.00 / $25.00 1.0M ctx |
| Grok-4 Heavy xAI | xAI's frontier — strong on reasoning + math, distinct voice | Native X (Twitter) integration for real-time data | Personality tuning is opinionated by default | — |
| DeepSeek-V4-Pro-Max DeepSeek | Best open-weight quality-to-price in the market | Frontier-adjacent quality at ~10× cheaper than US frontier | Routing through PRC providers may be a data-residency concern | $1.74 / $3.48 1.0M ctx |
Anthropic preview model — early-access benchmark only
Google's most capable widely-available model
Alibaba's newest — strongest open-weight Asian frontier
Frontier reasoning + nuanced long-form prose
xAI's frontier — strong on reasoning + math, distinct voice
Best open-weight quality-to-price in the market
Anthropic preview model — early-access benchmark only
When to useEvaluation and benchmark comparison only — not for production.
Google's most capable widely-available model
When to useResearch, document QA, anything that needs grounded citations.
Alibaba's newest — strongest open-weight Asian frontier
When to useMultilingual workloads; open-weight evaluations.
Frontier reasoning + nuanced long-form prose
When to useWhen output quality matters more than cost or latency.
xAI's frontier — strong on reasoning + math, distinct voice
When to useWhen you need real-time social-data grounding or want a non-mainstream alternative.
Best open-weight quality-to-price in the market
When to useCost-sensitive workloads at scale; on-prem requirements.
As of June 2026, Claude Mythos Preview by Anthropic leads the math leaderboard with a score of 62.6, followed by Claude Fable 5 (56.1) and Gemini 3.1 Pro (55.5). These rankings combine results from GSM8K (grade-school word problems), MATH (competition math), and AIME-style evaluations sourced from official model cards and independent reproductions.
The top math models almost universally use extended reasoning — they spend extra compute generating internal chains of thought before answering. That lifts accuracy on hard problems by 10–30% but adds 2–5x to latency and cost. For grade-school arithmetic, a standard model is just as accurate and far faster.
Rankings draw from 74 math benchmarks that test different skills: word problems, multi-step algebra, geometry, calculus, competition-level reasoning, and proof construction. Each benchmark contributes to a model's overall score, weighted by how decisive the benchmark is — competition math like MATH and AIME differentiates frontier models more than saturated benchmarks like GSM8K, where the top 10 all score above 95%.
Scores are normalized so benchmarks measured on different scales (percentage correct, problems solved, pass@1) can be compared directly. We source results from official model cards first, then independent reproductions when available — independently reproduced scores get higher weight because self-reported numbers occasionally use favorable evaluation conditions.
The conservative ranking penalizes models with sparse benchmark coverage. A model that only reports GSM8K can't be ranked against one tested across the full math suite — uncertainty bands stay wide until enough benchmark evidence accumulates.
For arithmetic, word problems, and grade-school math, any of the top 10 models will do — they're all above 95% on GSM8K and the differences are within noise. Pick the cheapest fast model. For high-school and undergraduate math (algebra, single-variable calculus, basic differential equations), the top 5 separate from the rest, and reasoning models start pulling ahead.
For competition math (AMC, AIME, Olympiad-level) and advanced topics (real analysis, topology, abstract algebra), only the top 2–3 reasoning models score reliably above 70%. The latency tradeoff is real — a single AIME problem can take 30–60 seconds — but accuracy on this class of problems still maxes out below 50% on the hardest IMO problems even for the best models. For symbolic computation specifically, Wolfram Alpha remains more reliable than any LLM. You can also compare models side-by-side before committing one to your workflow.
As of June 2026, Claude Mythos Preview leads math benchmarks with a score of 62.6, followed by Claude Fable 5 (56.1) and Gemini 3.1 Pro (55.5). All three use extended reasoning, adding 2-5x latency — for latency-sensitive workloads, the fastest model above 90% accuracy is often the better choice.
Ranked by 74 benchmarks including MATH, GSM8K, and AIME competition-level evaluations, sourced from official model cards and independent reproductions.
It depends on difficulty. For word problems and arithmetic, the top 5 models all score above 95% on GSM8K — differences are negligible. For competition math (AMC/AIME), only the top 2-3 score above 70% on MATH-5 problems requiring creative insight. Check the leaderboard above for current rankings.
Yes. Top models handle single-variable calculus, standard differential equations, and integration techniques reliably. Performance drops on multi-variable calculus, partial differential equations, and problems requiring geometric intuition. For symbolic computation, dedicated tools like Wolfram Alpha remain more reliable.
They serve different purposes. AI models explain reasoning step-by-step and handle word problems naturally. Wolfram Alpha excels at precise symbolic computation and doesn't make arithmetic errors. For learning and tutoring, AI models are better. For guaranteed-correct symbolic answers, Wolfram Alpha is better.
Reasoning models (like o-series) spend more compute per problem, generating internal chains of thought before answering. They outperform standard models by 10-30% on hard math but cost 3-5x more and respond slower. For simple arithmetic, a standard model is sufficient and much faster.
On grade-school word problems (GSM8K), top models exceed 95% accuracy. On competition math (MATH benchmark), scores range from 50-85% depending on difficulty level. On Olympiad problems (IMO level), even the best models score below 50%. Accuracy drops sharply as problems require more creative reasoning.
