Coding · Mistral AI

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

Mixtral scores 60.3 on SWE-bench Verified, placing it #321 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
60.3
0–100 scale
Rank
#321
of 600
Percentile
47th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
21.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Mixtral 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, Mixtral sits at #321 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 21.4 points. The median model in this index scores 61.0, which puts Mixtral below the midpoint.

Mistral AI models average 54.7 on this benchmark. Mixtral is 5.6 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. Mixtral is 6.1 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 60.3 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 $3.48 per million input tokens, Mixtral 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 Base ByteDance 16384 $3.14 72.2
Firefunction Max Fireworks AI 32768 $9.12 73.2
Mixtral Groq Chat Groq 8192 $3.09 72.0
Yi-Large Instruct 01.AI 32768 $3.48 73.4
Mixtral Mistral AI 65536 $3.48 73.7
Nova Lite Max Amazon 200000 $9.79 73.4
Llama 3 DeepInfra DeepInfra 128000 $3.67 74.0

Other benchmarks for Mixtral

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.3 269 55th
GPQA Diamond 71.3 234 61th
HumanEval 75.9 330 45th
MATH-500 78.5 249 59th
AIME 2025 72.2 215 64th
LiveCodeBench 62.5 342 43th
IFEval 81.2 298 50th
TruthfulQA 76.4 297 51th
HellaSwag 82.8 326 46th
MMMU 72.7 228 62th
Arena Elo 1363.2 337 44th

FAQ

What score does Mixtral get on SWE-bench Verified?

Mixtral scores 60.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Mixtral rank on SWE-bench Verified?

It ranks #321 of 600 models, in the 47th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Mixtral good at SWE-bench Verified?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Mixtral cost?

Mixtral costs $3.48 per million input tokens and $12.73 per million output tokens.

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