Coding · Mistral AI

Magistral 1.5B on LiveCodeBench: score, rank and what it means

Magistral 1.5B scores 36.9 on LiveCodeBench, placing it #557 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
36.9
0–100 scale
Rank
#557
of 600
Percentile
7th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
48.2
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Magistral 1.5B highlighted. Longer bars mean higher scores.

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, Magistral 1.5B sits at #557 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 48.2 points. The median model in this index scores 64.7, which puts Magistral 1.5B below the midpoint.

Mistral AI models average 58.1 on this benchmark. Magistral 1.5B is 21.2 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 41.2. Magistral 1.5B is 4.3 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 36.9 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 $0.67 per million input tokens, Magistral 1.5B is below the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Nemotron 4 1.5B NVIDIA 32768 $0.49 50.8
DeepSeek-Coder 1.5B DeepSeek 16384 $0.58 51.0
Mixtral Turbo 7B Together AI 8192 $0.68 53.2
Nemotron Nano 8B NVIDIA 8192 $0.46 48.1
Magistral 1.5B Mistral AI 4096 $0.67 52.7
Phi-4 1.5B Microsoft 8192 $0.42 48.1
Codestral 1.5B Mistral AI 8192 $0.52 49.9

Other benchmarks for Magistral 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 59.2 536 11th
GPQA Diamond 46.8 550 8th
SWE-bench Verified 40.3 519 14th
HumanEval 58.0 524 13th
MATH-500 52.9 534 11th
AIME 2025 39.3 545 9th
IFEval 62.6 549 9th
TruthfulQA 59.4 537 11th
HellaSwag 71.4 513 15th
MMMU 52.8 523 13th
Arena Elo 1208.3 546 9th

FAQ

What score does Magistral 1.5B get on LiveCodeBench?

Magistral 1.5B scores 36.9 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Magistral 1.5B rank on LiveCodeBench?

It ranks #557 of 600 models, in the 7th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Magistral 1.5B good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench 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 Magistral 1.5B cost?

Magistral 1.5B costs $0.67 per million input tokens and $2.43 per million output tokens.

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