Coding · Mistral AI

Codestral Chat on SWE-bench Verified: score, rank and what it means

Codestral Chat scores 62.8 on SWE-bench Verified, placing it #256 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.8
0–100 scale
Rank
#256
of 600
Percentile
57th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
18.9
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Codestral Chat 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, Codestral Chat sits at #256 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 18.9 points. The median model in this index scores 61.0, which puts Codestral Chat above the midpoint.

Mistral AI models average 54.7 on this benchmark. Codestral Chat is 8.1 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among multimodal models, the average is 66.4. Codestral Chat is 3.6 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 62.8 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 $3.89 per million input tokens, Codestral Chat is below 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
Llama 3 Anyscale Anyscale 65536 $3.94 74.7
Sonar Max Perplexity 32768 $11.60 77.8
Llama 3 Baseten Base Baseten 16384 $3.16 72.4
Llama 3 Baseten Chat Baseten 200000 $3.55 73.2
Codestral Chat Mistral AI 32768 $3.89 75.8
Llama 3 Baseten Instruct Baseten 200000 $3.91 76.2
Llama 3 Lambda Lambda 65536 $3.90 75.6

Other benchmarks for Codestral Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.1 180 70th
GPQA Diamond 70.0 260 57th
HumanEval 82.3 178 70th
MATH-500 76.8 283 53th
AIME 2025 73.9 184 69th
LiveCodeBench 67.0 248 59th
IFEval 82.0 276 54th
TruthfulQA 79.2 209 65th
HellaSwag 86.3 204 66th
MMMU 73.1 215 64th
Arena Elo 1389.9 250 58th

FAQ

What score does Codestral Chat get on SWE-bench Verified?

Codestral Chat scores 62.8 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Codestral Chat rank on SWE-bench Verified?

It ranks #256 of 600 models, in the 57th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Codestral Chat 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 Codestral Chat cost?

Codestral Chat costs $3.89 per million input tokens and $18.19 per million output tokens.

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