Coding · Cohere

Command A Pro on SWE-bench Verified: score, rank and what it means

Command A Pro scores 71.2 on SWE-bench Verified, placing it #90 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
71.2
0–100 scale
Rank
#90
of 600
Percentile
85th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
10.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Command A Pro highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
3 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
4 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 o4 Max OpenAI 2000000 $19.51 86.2
8 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On SWE-bench Verified, Command A Pro sits at #90 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 10.5 points. The median model in this index scores 61.0, which puts Command A Pro above the midpoint.

Cohere models average 56.0 on this benchmark. Command A Pro is 15.2 points above that provider line, so it is a stronger result than the average Cohere model.

Among multimodal models, the average is 66.4. Command A Pro is 4.8 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 71.2 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 Pro is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Gemini 3 Flash Vision Google DeepMind 400000 $15.06 81.9
Grok 3 Pro xAI 400000 $16.61 83.8
Grok 3 Turbo xAI 128000 $15.97 83.1
Mistral Large 3 Mistral AI 128000 $5.41 81.7
Command A Pro Cohere 200000 $14.28 81.3
DeepSeek-R1 Base DeepSeek 400000 $5.05 80.1
Doubao 1.5 Pro Pro ByteDance 128000 $14.10 80.2

Other benchmarks for Command A Pro

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.3 138 77th
GPQA Diamond 79.1 70 88th
HumanEval 87.5 78 87th
MATH-500 83.1 144 76th
AIME 2025 78.2 112 81th
LiveCodeBench 74.0 109 82th
IFEval 89.0 82 86th
TruthfulQA 83.3 101 83th
HellaSwag 90.1 84 86th
MMMU 77.2 111 82th
Arena Elo 1434.6 108 82th

FAQ

What score does Command A Pro get on SWE-bench Verified?

Command A Pro scores 71.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Command A Pro rank on SWE-bench Verified?

It ranks #90 of 600 models, in the 85th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Command A Pro good at SWE-bench Verified?

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 SWE-bench Verified score calculated?

SWE-bench Verified is a coding 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 Pro cost?

Command A Pro costs $14.28 per million input tokens and $68.05 per million output tokens.

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