Coding · Mistral AI

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

Codestral Base scores 62.6 on SWE-bench Verified, placing it #264 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.6
0–100 scale
Rank
#264
of 600
Percentile
56th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.1
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Codestral Base 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 Base sits at #264 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.1 points. The median model in this index scores 61.0, which puts Codestral Base above the midpoint.

Mistral AI models average 54.7 on this benchmark. Codestral Base is 7.9 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among open-weight models, the average is 55.5. Codestral Base is 7.1 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 62.6 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 $3.31 per million input tokens, Codestral Base 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
Seed-OSS ByteDance 32768 $3.74 75.3
Llama 3 Baseten Baseten 128000 $3.62 74.9
Phi-4 Base Microsoft 32768 $3.15 71.2
Phi-4 Instruct Microsoft 128000 $3.79 75.3
Codestral Base Mistral AI 8192 $3.31 73.1
Qwen3-Max Mini Alibaba Cloud 128000 $11.13 76.5
Yi-Lightning Chat 01.AI 32768 $3.79 75.5

Other benchmarks for Codestral Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.1 348 42th
GPQA Diamond 70.4 252 58th
HumanEval 79.7 240 60th
MATH-500 77.4 267 56th
AIME 2025 68.7 283 53th
LiveCodeBench 65.7 279 54th
IFEval 79.2 347 42th
TruthfulQA 74.3 347 42th
HellaSwag 83.3 311 48th
MMMU 68.3 339 44th
Arena Elo 1371.7 310 48th

FAQ

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

Codestral Base scores 62.6 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Codestral Base rank on SWE-bench Verified?

It ranks #264 of 600 models, in the 56th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Codestral Base 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 Base cost?

Codestral Base costs $3.31 per million input tokens and $16.95 per million output tokens.

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