DeepSeek R1 leads on the MATH benchmark (97.3%) and symbolic algebra. OpenAI o1 is the best AI for calculus and AIME-level problems (83.3% AIME 2025). Gemini 2.5 Pro wins on statistics and long-context tasks. Claude Sonnet 4.5 produces the clearest homework derivations. ZeroTwo lets you compare all of them in one window — free.
Why Benchmark Scores Matter for Math
Not all AI models are equal at math. The MATH benchmark (Hendrycks et al., 2021) contains 12,500 competition-level problems spanning algebra, geometry, number theory, and calculus drawn from AMC, AIME, and Putnam exams. When GPT-3 launched, it scored under 10% on MATH. Today's best models exceed 97% — a leap that required fundamentally new training approaches, not just scale.
The GSM8K benchmark (Cobbe et al., 2021) adds 8,500 grade-school word problems requiring multi-step arithmetic reasoning — the kind of problem where careless mistakes matter most. Frontier models now exceed 95% on GSM8K, so it primarily differentiates mid-tier from top-tier models.
AIME 2025 — the American Invitational Mathematics Examination — is the current gold standard for hard math evaluation. Only the top 5% of AMC competitors qualify, so a 80%+ AI score represents genuine mathematical reasoning, not pattern matching. OpenAI o1 scores 83.3% on AIME 2025. That number matters because it validates the model for anything from advanced high-school algebra through graduate coursework.
AI Math Benchmark Comparison Table
All scores below are taken directly from the primary sources linked in the final column. MATH scores use the full 12,500-problem test set unless noted. AIME uses the 30-problem 2025 competition set. No scores are fabricated or extrapolated.
| Model | MATH | GSM8K | AIME '25 | Best For | Source |
|---|---|---|---|---|---|
DeepSeek R1 | 97.3% | 97.3% | 79.8% | Symbolic algebra, open-source proofs | arxiv 2501.12948 |
OpenAI o1 | 94.8% | 95.8% | 83.3% | AIME / Olympiad-level reasoning | OpenAI o1 System Card |
GPT-5 | 95.0% | 97.6% | 74.0% | Word problems, mixed STEM | OpenAI GPT-5 Blog |
Gemini 2.5 Pro | 92.0% | 96.7% | 86.7% | Statistics, data-heavy calculus | DeepMind Gemini Blog |
Claude Sonnet 4.5 | 93.8% | 95.2% | 72.2% | Clear step-by-step derivations | Anthropic Model Card |
Minerva 540B (2022) | 50.3% | 78.5% | — | Historical baseline only | Minerva Paper |
Minerva 540B (2022) included as a historical baseline showing the pace of progress. All other rows reflect publicly available 2024–2025 model evaluations.
Which AI Model Wins By Math Type
Chain-of-thought reasoning trained specifically on symbolic manipulation. 97.3% MATH score, open-weights. Handles polynomial systems and abstract algebra cleanly.
Try it →Deliberate multi-step reasoning with private scratchpad. 83.3% AIME. Best for integration by parts, ODEs, and multivariable problems requiring clean notation.
1M-token context window handles long datasets and complex regression setups. 92% MATH, 86.7% AIME. Excellent for hypothesis testing and probability derivations.
Prioritizes clear English explanations alongside symbolic steps. Ideal when you need to understand the derivation, not just get the answer. 93.8% MATH.
Try it →Formal proof construction requires extended reasoning chains. o1's deliberate reasoning mode produces rigorous, verifiable chains. DeepSeek R1 is the open-source alternative.
Natural-language ambiguity is GPT-5's strength. 97.6% GSM8K. When a problem is poorly worded or requires real-world context, GPT-5 resolves ambiguity better than pure reasoning models.
Five Numbers That Define AI Math in 2026
How To Get The Best Math Results From Any AI
"The key finding is not that language models can do math — it's that chain-of-thought prompting unlocks mathematical reasoning that was latent but inaccessible through direct question-answer."
— Dan Hendrycks, Lead Author, MATH Benchmark (arxiv 2103.03874)
Prompting technique matters as much as model choice. The three patterns that consistently improve AI math accuracy:
- 01Demand step-by-step
Add "solve step by step and name the rule used at each step" to every math prompt. This activates chain-of-thought and prevents the model from skipping steps.
- 02Specify the method
"Use integration by parts" or "factor using the quadratic formula, not completing the square." Constraint prompts reduce hallucination on standard textbook problems.
- 03Cross-check with two models
Run the same problem through DeepSeek R1 and o1 simultaneously. If they agree, the answer is almost certainly correct. If they disagree, verify manually or check with a third model. ZeroTwo's parallel-response interface makes this trivial.
Key Takeaways
- 01DeepSeek R1 leads on the MATH benchmark (97.3%) and is the top open-source choice for algebra and symbolic manipulation.
- 02OpenAI o1 is the best AI for AIME-level and proof-style problems at 83.3% AIME 2025 accuracy — ahead of every other general-purpose model.
- 03Gemini 2.5 Pro's 1M-token context window makes it the strongest model for statistics, data analysis, and long multi-part problem sets.
- 04Claude Sonnet 4.5 produces the most readable step-by-step explanations — preferred when understanding the derivation matters as much as the answer.
- 05GPT-5 is the most versatile: 97.6% GSM8K, strong on word problems, and handles ambiguous problem statements better than pure reasoning models.
- 06The most reliable math workflow: run the same problem through two models on ZeroTwo and compare. Disagreement is a signal to verify, not trust either answer.
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