math benchmark
MathVista-Mini is a smaller version of the MathVista benchmark for evaluating mathematical reasoning in visual contexts.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score90.10% | Percentile100.00% | Participants25 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score87.80% | Percentile95.83% | Participants25 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score87.40% | Percentile91.67% | Participants25 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score87.40% | Percentile87.50% | Participants25 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score86.40% | Percentile83.33% | Participants25 | EvidenceC | Evaluated |
| Rank06 | ModelAC | Score86.20% | Percentile79.17% | Participants25 | EvidenceC | Evaluated |
| Rank07 | ModelAC | Score85.90% | Percentile75.00% | Participants25 | EvidenceC | Evaluated |
| Rank08 | ModelAC | Score85.80% | Percentile70.83% | Participants25 | EvidenceC | Evaluated |
| Rank09 | ModelLA | Score85.00% | Percentile66.67% | Participants25 | EvidenceC | Evaluated |
| Rank10 | ModelAC | Score84.90% | Percentile62.50% | Participants25 | EvidenceC | Evaluated |
| Rank11 | ModelAC | Score83.80% | Percentile58.33% | Participants25 | EvidenceC | Evaluated |
| Rank12 | ModelAC | Score81.90% | Percentile54.17% | Participants25 | EvidenceC | Evaluated |
| Rank13 | ModelAC | Score81.40% | Percentile50.00% | Participants25 | EvidenceC | Evaluated |
| Rank14 | ModelAC | Score80.10% | Percentile45.83% | Participants25 | EvidenceC | Evaluated |
| Rank15 | ModelAC | Score79.50% | Percentile41.67% | Participants25 | EvidenceC | Evaluated |
| Rank16 | ModelAC | Score77.20% | Percentile37.50% | Participants25 | EvidenceC | Evaluated |
| Rank17 | ModelAC | Score74.80% | Percentile33.33% | Participants25 | EvidenceC | Evaluated |
| Rank18 | ModelAC | Score74.70% | Percentile29.17% | Participants25 | EvidenceC | Evaluated |
| Rank19 | ModelAC | Score73.70% | Percentile25.00% | Participants25 | EvidenceC | Evaluated |
| Rank20 | ModelAC | Score70.50% | Percentile20.83% | Participants25 | EvidenceC | Evaluated |
| Rank21 | ModelLA | Score68.50% | Percentile16.67% | Participants25 | EvidenceC | Evaluated |
| Rank22 | ModelAC | Score68.20% | Percentile12.50% | Participants25 | EvidenceC | Evaluated |
| Rank23 | ModelGO | Score67.60% | Percentile8.33% | Participants25 | EvidenceC | Evaluated |
| Rank24 | ModelGO | Score62.90% | Percentile4.17% | Participants25 | EvidenceC | Evaluated |
| Rank25 | ModelGO | Score50.00% | Percentile0.00% | Participants25 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What MathVista-Mini measures and how its scores work.
MathVista-Mini consists of examples derived from multimodal datasets involving mathematics and combines diverse mathematical and visual tasks.
It measures the ability to solve problems requiring both visual understanding and mathematical reasoning, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MathVista-Mini.
Kimi K2.5 is currently ranked first with 90.10%.
The current leaders are Kimi K2.5 (90.10%), Qwen3.5-27B (87.80%), and Qwen3.5-122B-A10B (87.40%).
Qwen3.6-35B-A3B has the lowest matched official input price at $0.25 input / $1.5 output per 1M tokens.
The fastest matched records are Gemma 3 27B (33.00 tok/s via DeepInfra), Gemma 3 12B (33.00 tok/s via DeepInfra), and Gemma 3 4B (33.00 tok/s via DeepInfra).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures the ability to solve problems requiring both visual understanding and mathematical reasoning, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
25 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis mathvista-mini AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi K2.5 currently leads MathVista-Mini with 90.10%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.