Competition math, formal proofs, research-level problem solving, and verifiable reasoning. This report blends public leaderboard signals into one task-specific composite score, then shows the best-fit use cases, evidence coverage, and decision context behind each ranked model.
Use this math ranking to compare AI models for quantitative reasoning, proof exploration, contest-style problems, tutoring, and solution checking.
Last updated: June 16, 2026
MethodologyWhat changed in this update
Page value
Math reasoning and solution-checking shortlist.
Data basis
6 public sources · 21 models
Ranking snapshot
2026-06-16
Claude Fable 5
95.9
2026-06-16
Best for
Evaluate
Avoid
All ranked models
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.
Best fit
Best overall pick when formal proof and research math both matter.
Strong evidence: 5/6 sources · 90% confidence
Adjusted score
95.9
Model
98
Confidence
90%
Best fit
Fast math assistance and high-volume quantitative problem solving.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
91.2
Model
100
Confidence
65%
Best fit
Extended-reasoning math explanations and proof-style drafts.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
91.2
Model
100
Confidence
65%
Best fit
High-effort OpenAI math reasoning and contest-style problem solving.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
90.5
Model
99
Confidence
65%
Best fit
Strong math explanations and symbolic reasoning drafts.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
90.5
Model
99
Confidence
65%
Best fit
Careful math reasoning where slower thinking-mode review is useful.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
90.5
Model
99
Confidence
65%
Best fit
Long-context math prompts, multimodal math, and Google ecosystem workflows.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
90.5
Model
99
Confidence
65%
Best fit
High-effort OpenAI math runs when exact reasoning settings can be controlled.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Premium math support and detailed solution review.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Thinking-mode solution planning and multi-step derivations.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Qwen math reasoning tests and cost-aware quantitative workflows.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Long-context math reading and open-weight oriented comparison.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Open-weight math shortlist testing and lower-cost deployment experiments.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.9
Model
98
Confidence
65%
Best fit
Preview-model Qwen math comparisons.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.2
Model
97
Confidence
65%
Best fit
Alternative math reasoning tests across structured prompts.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.2
Model
97
Confidence
65%
Best fit
Fast math drafting and lower-latency reasoning workflows.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.2
Model
97
Confidence
65%
Best fit
Reasoning-forward Kimi math prompts and long-context derivations.
Low evidence: 1/6 sources · 65% confidence
Adjusted score
89.2
Model
97
Confidence
65%
Best fit
Formal math and careful proof-style reasoning.
Full evidence: 6/6 sources · 100% confidence
Adjusted score
84.2
Model
84.2
Confidence
100%
Best fit
Hard math plus general advanced reasoning.
Strong evidence: 5/6 sources · 90% confidence
Adjusted score
82.5
Model
83.1
Confidence
90%
Best fit
AIME-style math and broad reasoning workflows.
Strong evidence: 5/6 sources · 90% confidence
Adjusted score
80.4
Model
80.8
Confidence
90%
Best fit
Competition math, long context, and multimodal math workflows.
Full evidence: 6/6 sources · 100% confidence
Adjusted score
78.3
Model
78.3
Confidence
100%
Decision guide
Questions
The top model is the strongest blended math pick for this snapshot, but different math domains can favor different models.
Yes. High-ranked models still produce wrong steps or overconfident answers, so important results should be verified independently.
Use a private problem set with known answers, score final accuracy and reasoning quality separately, and include prompts from your actual use case.
Method note
The top model is the best blended pick 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 real tasks before standardizing. See the methodology and editorial policy for source selection and correction standards.
