Mathematics · Cohere

Command A Max on MATH-500: score, rank and what it means

Command A Max scores 85.7 on MATH-500, placing it #97 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
85.7
0–100 scale
Rank
#97
of 600
Percentile
84th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
11.0
points

Score distribution

The chart shows the top 10 models on MATH-500, with Command A Max highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Command A Max sits at #97 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 11.0 points. The median model in this index scores 76.0, which puts Command A Max above the midpoint.

Cohere models average 70.8 on this benchmark. Command A Max is 14.9 points above that provider line, so it is a stronger result than the average Cohere model.

Among multimodal models, the average is 81.3. Command A Max is 4.4 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 85.7 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $14.28 per million input tokens, Command A Max is above the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
MiniMax-M2 Chat MiniMax 200000 $5.02 80.2
Reka Core Pro Reka AI 65536 $13.44 79.9
Claude Haiku 4.5 Reasoning Anthropic 128000 $15.11 81.4
Command A Turbo Cohere 32768 $13.53 79.8
Command A Max Cohere 400000 $14.28 79.9
Reka Core Max Reka AI 128000 $13.41 79.3
Grok 3 Turbo xAI 128000 $15.97 83.1

Other benchmarks for Command A Max

BenchmarkScoreRankPercentileRelative
MMLU-Pro 84.3 73 88th
GPQA Diamond 74.5 153 75th
SWE-bench Verified 69.7 116 81th
HumanEval 83.4 147 76th
AIME 2025 77.2 126 79th
LiveCodeBench 73.1 124 79th
IFEval 86.7 140 77th
TruthfulQA 82.2 125 79th
HellaSwag 87.1 171 72th
MMMU 74.6 166 72th
Arena Elo 1443.7 91 85th

FAQ

What score does Command A Max get on MATH-500?

Command A Max scores 85.7 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Command A Max rank on MATH-500?

It ranks #97 of 600 models, in the 84th percentile. The leader is GPT-5.2 Max at 96.7.

Is Command A Max good at MATH-500?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the MATH-500 score calculated?

MATH-500 is a mathematics benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does Command A Max cost?

Command A Max costs $14.28 per million input tokens and $70.22 per million output tokens.

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