Coding · Mistral AI

Mistral Large 3 8B on SWE-bench Verified: score, rank and what it means

Mistral Large 3 8B scores 48.9 on SWE-bench Verified, placing it #430 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
48.9
0–100 scale
Rank
#430
of 600
Percentile
28th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
32.8
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Mistral Large 3 8B 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, Mistral Large 3 8B sits at #430 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 32.8 points. The median model in this index scores 61.0, which puts Mistral Large 3 8B below the midpoint.

Mistral AI models average 54.7 on this benchmark. Mistral Large 3 8B is 5.8 points below that provider line, so it is a weaker result than the average Mistral AI model.

Among small open-weight models, the average is 39.0. Mistral Large 3 8B is 9.9 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 48.9 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 $1.62 per million input tokens, Mistral Large 3 8B 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
Hunyuan-Turbo Nano Tencent 32768 $4.65 62.7
Embed 4 Reasoning Cohere 4096 $5.50 65.3
Mistral Large 3 7B Mistral AI 32768 $1.44 61.1
Zephyr Chat Hugging Face 4096 $1.49 61.3
Mistral Large 3 8B Mistral AI 16384 $1.62 61.6
Auto Mini OpenRouter 4096 $4.24 61.6
Firesearch Mini Fireworks AI 32768 $3.94 61.5

Other benchmarks for Mistral Large 3 8B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 64.7 459 24th
GPQA Diamond 55.7 453 25th
HumanEval 66.5 425 29th
MATH-500 61.0 452 25th
AIME 2025 53.3 430 28th
LiveCodeBench 52.1 429 29th
IFEval 73.7 408 32th
TruthfulQA 65.7 449 25th
HellaSwag 76.3 422 30th
MMMU 59.7 431 28th
Arena Elo 1301.3 417 31th

FAQ

What score does Mistral Large 3 8B get on SWE-bench Verified?

Mistral Large 3 8B scores 48.9 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Mistral Large 3 8B rank on SWE-bench Verified?

It ranks #430 of 600 models, in the 28th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Mistral Large 3 8B 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 Mistral Large 3 8B cost?

Mistral Large 3 8B costs $1.62 per million input tokens and $6.82 per million output tokens.

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