Coding · Alibaba Cloud

Qwen3-Coder Instruct on LiveCodeBench: score, rank and what it means

Qwen3-Coder Instruct scores 71.9 on LiveCodeBench, placing it #149 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
71.9
0–100 scale
Rank
#149
of 600
Percentile
75th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
13.2
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Qwen3-Coder Instruct 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, Qwen3-Coder Instruct sits at #149 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 13.2 points. The median model in this index scores 64.7, which puts Qwen3-Coder Instruct above the midpoint.

Alibaba Cloud models average 64.0 on this benchmark. Qwen3-Coder Instruct is 7.9 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among coding models, the average is 59.3. Qwen3-Coder Instruct is 12.6 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 71.9 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 $4.66 per million input tokens, Qwen3-Coder Instruct is above 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
Llama 4 Scout Meta AI 200000 $4.90 79.3
Reka Core Turbo Reka AI 32768 $12.58 78.4
ERNIE 4.0 Turbo Baidu 65536 $12.00 78.0
ERNIE 4.5 Reasoning Baidu 1000000 $14.36 79.8
Qwen3-Coder Instruct Alibaba Cloud 128000 $4.66 78.1
Command A Cohere 200000 $13.30 79.6
Hunyuan-Large Tencent 65536 $4.33 78.2

Other benchmarks for Qwen3-Coder Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.5 136 77th
GPQA Diamond 71.7 228 62th
SWE-bench Verified 69.3 126 79th
HumanEval 82.2 183 70th
MATH-500 80.5 201 67th
AIME 2025 72.2 216 64th
IFEval 85.3 180 70th
TruthfulQA 81.0 158 74th
HellaSwag 89.4 104 83th
MMMU 74.1 190 68th
Arena Elo 1418.7 154 74th

FAQ

What score does Qwen3-Coder Instruct get on LiveCodeBench?

Qwen3-Coder Instruct scores 71.9 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Qwen3-Coder Instruct rank on LiveCodeBench?

It ranks #149 of 600 models, in the 75th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Qwen3-Coder Instruct good at LiveCodeBench?

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 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 Qwen3-Coder Instruct cost?

Qwen3-Coder Instruct costs $4.66 per million input tokens and $24.30 per million output tokens.

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