High score
Means the model is worth testing on your math domain, not that every shown derivation is correct.
Public model-signal snapshot
Best AI for Math
Competition math, formal proofs, research-level problem solving, and verifiable reasoning. This page is a starting point, not proof. It turns public source rows into a task-specific candidate score, then shows where each model fits, which sources covered it, and what to check on your own tasks.
Start here when you need a math-model shortlist for quantitative reasoning, proof exploration, contest-style problems, tutoring, and solution checking.
Last updated: July 2, 2026
MethodologyWhat changed in this update
- Added a visible update note for the math score snapshot.
- Rechecked source weighting across proof, contest math, AIME, and general reasoning signals.
- Expanded guidance around independent verification of final answers and intermediate reasoning steps.
Use this for
Math candidates to verify on your own problem set.
Public rows
6 public sources · 21 models
Score snapshot
2026-07-02
First candidate to test
Claude Fable 5
Adjusted score
95.9
Snapshot
2026-07-02
Best for
- Checking solutions, exploring proof strategies, and solving structured math prompts.
- Comparing reasoning models across formal benchmarks and public math evaluations.
Evaluate
- Verify final answers independently with symbolic tools, calculators, or known solutions.
- Check whether intermediate steps are consistent, not just whether the final answer looks plausible.
Avoid
- Trusting long reasoning chains without external verification.
- Using a general chat model as a grading authority without a clear rubric.
How to read this score
Coverage gap
Contest math, proof, tutoring, and symbolic tasks should be checked separately before adoption.
Hands-on check
Score final answers and reasoning quality separately, then verify with known solutions or symbolic tools.
Validation playbook
Run this check before trusting the Best AI for Math shortlist
Use this shortlist to pick finalists, then run a small, repeatable validation pass so the final choice matches your workflow, risk tolerance, cost target, and review policy.
Separate answer and reasoning
Score correctness twice
Grade the final answer and the derivation independently because a model can reach the right number with fragile or misleading reasoning.
Use domain examples
Do not rely on one benchmark style
Test contest questions, proof-style prompts, tutoring explanations, and symbolic calculations separately if your product needs them.
Verify with tools
Use known solutions or solvers
Check arithmetic, algebra, and edge cases with trusted answers, symbolic tools, or a second independent method.
Watch confidence
Penalize polished wrong answers
A model that cannot flag uncertainty or ask for missing assumptions is risky even when the explanation reads well.
All model candidates
Full scored model list
Showing 21 models with at least one source score. Rows are ordered by Bayesian-smoothed adjusted score; missing source rows stay n/a instead of counting as zero.
1
Claude Fable 5
AnthropicProprietary APIBest fit
Best overall pick when formal proof and research math both matter.
Source coverage5/6
Strong evidence: 5/6 sources · 90% confidence
Mathn/aVals ProofBench100Surge AI Riemann-bench100Vellum AIME 202594Artificial Analysis AIME 202594AA Index100
Adjusted score
95.9
Model
98
Confidence
90%
2
Gemini 3.5 Flash
GoogleProprietary API and appsBest fit
Fast math assistance and high-volume quantitative problem solving.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math100Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
91.2
Model
100
Confidence
65%
3
Claude Opus 4.6 Thinking
AnthropicProprietary APIBest fit
Extended-reasoning math explanations and proof-style drafts.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math100Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
91.2
Model
100
Confidence
65%
4
GPT-5.4 High
OpenAIProprietary APIBest fit
High-effort OpenAI math reasoning and contest-style problem solving.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math99Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
90.5
Model
99
Confidence
65%
5
Claude Opus 4.6
AnthropicProprietary APIBest fit
Strong math explanations and symbolic reasoning drafts.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math99Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
90.5
Model
99
Confidence
65%
6
Claude Opus 4.8 Thinking
AnthropicProprietary APIBest fit
Careful math reasoning where slower thinking-mode review is useful.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math99Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
90.5
Model
99
Confidence
65%
7
Gemini 3.1 Pro Preview
GoogleProprietary API and appsBest fit
Long-context math prompts, multimodal math, and Google ecosystem workflows.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math99Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
90.5
Model
99
Confidence
65%
8
GPT-5.5 High
OpenAIProprietary APIBest fit
High-effort OpenAI math runs when exact reasoning settings can be controlled.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
9
Claude Opus 4.7
AnthropicProprietary APIBest fit
Premium math support and detailed solution review.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
10
Claude Opus 4.7 Thinking
AnthropicProprietary APIBest fit
Thinking-mode solution planning and multi-step derivations.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
11
Qwen3.7 Max Preview
AlibabaProprietary APIBest fit
Qwen math reasoning tests and cost-aware quantitative workflows.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
12
Kimi K2.6
MoonshotModified MITBest fit
Long-context math reading and open-weight oriented comparison.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
13
Mimo V2.5 Pro
XiaomiMITBest fit
Open-weight math shortlist testing and lower-cost deployment experiments.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math98Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.9
Model
98
Confidence
65%
14
Qwen3.6 Max Preview
AlibabaProprietary APIBest fit
Preview-model Qwen math comparisons.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math97Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.2
Model
97
Confidence
65%
15
MiniMax M3
MiniMaxProprietary APIBest fit
Alternative math reasoning tests across structured prompts.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math97Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.2
Model
97
Confidence
65%
16
Gemini 3 Flash
GoogleProprietary API and appsBest fit
Fast math drafting and lower-latency reasoning workflows.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math97Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.2
Model
97
Confidence
65%
17
Kimi K2.5 Thinking
MoonshotModified MITBest fit
Reasoning-forward Kimi math prompts and long-context derivations.
Source coverage1/6
Low evidence: 1/6 sources · 65% confidence
Math97Vals ProofBenchn/aSurge AI Riemann-benchn/aVellum AIME 2025n/aArtificial Analysis AIME 2025n/aAA Indexn/a
Adjusted score
89.2
Model
97
Confidence
65%
18
Claude Opus 4.8
AnthropicProprietary APIBest fit
Formal math and careful proof-style reasoning.
Source coverage6/6
Full evidence: 6/6 sources · 100% confidence
Math98Vals ProofBench90Surge AI Riemann-bench47Vellum AIME 202594Artificial Analysis AIME 202590AA Index94
Adjusted score
84.2
Model
84.2
Confidence
100%
19
GPT-5.5
OpenAIProprietary APIBest fit
Hard math plus general advanced reasoning.
Source coverage5/6
Strong evidence: 5/6 sources · 90% confidence
Mathn/aVals ProofBench82Surge AI Riemann-bench76Vellum AIME 202588Artificial Analysis AIME 202588AA Index92
Adjusted score
82.5
Model
83.1
Confidence
90%
20
GPT-5.2
OpenAIProprietary APIBest fit
AIME-style math and broad reasoning workflows.
Source coverage5/6
Strong evidence: 5/6 sources · 90% confidence
Mathn/aVals ProofBench78Surge AI Riemann-bench58Vellum AIME 2025100Artificial Analysis AIME 2025100AA Index90
Adjusted score
80.4
Model
80.8
Confidence
90%
21
Gemini 3 Pro
GoogleProprietary API and appsBest fit
Competition math, long context, and multimodal math workflows.
Source coverage6/6
Full evidence: 6/6 sources · 100% confidence
Math97Vals ProofBench78Surge AI Riemann-bench28Vellum AIME 2025100Artificial Analysis AIME 202595AA Index88
Adjusted score
78.3
Model
78.3
Confidence
100%
Decision guide
How to choose from this Best AI for Math shortlist
Best for
- Checking solutions, exploring proof strategies, and solving structured math prompts.
- Comparing reasoning models across formal benchmarks and public math evaluations.
- Shortlisting models for tutoring, STEM assistants, and internal math test sets.
Evaluate
- Verify final answers independently with symbolic tools, calculators, or known solutions.
- Check whether intermediate steps are consistent, not just whether the final answer looks plausible.
- Use topic-specific test sets for algebra, geometry, statistics, proofs, and contest math.
Avoid
- Trusting long reasoning chains without external verification.
- Using a general chat model as a grading authority without a clear rubric.
- Assuming a model that wins on one math benchmark will dominate every math domain.
Related decisions
What to check after this shortlist
Questions
Best AI for Math FAQ
What is the best AI for math?
The first row has the strongest public math signals in this snapshot, but different math domains can favor different models.
Can AI models make math mistakes even with high scores?
Yes. High-scoring models still produce wrong steps or overconfident answers, so important results should be verified independently.
How should I test an AI math model?
Use a private problem set with known answers, score final accuracy and reasoning quality separately, and include prompts from your actual use case.
Is the best AI for math the same as the best reasoning model?
Not always. Math requires answer accuracy, symbolic consistency, and step checking; a strong general reasoning model can still fail on specific math domains.
Should I use AI for tutoring math?
AI can help explain steps and generate practice, but students should verify answers and instructors should define where AI assistance is allowed.
How do API costs change for math and reasoning tasks?
Reasoning tasks often produce longer outputs and retries. Compare models with realistic prompts and final answer checks before choosing a default API model.
Other model shortlists
Method note
Let the first row tell you what to test first
The first row has the strongest public-signal score for this query snapshot, but model choice should still account for price, latency, privacy, context length, tool access, safety settings, and your own benchmark prompts. Use this page to reduce the search space, then run a small evaluation on your tasks before making one your default. When speed, RAM, or offline use matters, check the machine-specific test records first. See the methodology and editorial policy for source selection and correction standards.