Coding · Alibaba Cloud

Qwen3-Coder Instruct on SWE-bench Verified: score, rank and what it means

Qwen3-Coder Instruct scores 69.3 on SWE-bench Verified, placing it #126 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
69.3
0–100 scale
Rank
#126
of 600
Percentile
79th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
12.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Qwen3-Coder Instruct 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, Qwen3-Coder Instruct sits at #126 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 12.4 points. The median model in this index scores 61.0, which puts Qwen3-Coder Instruct above the midpoint.

Alibaba Cloud models average 60.8 on this benchmark. Qwen3-Coder Instruct is 8.5 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among coding models, the average is 56.2. Qwen3-Coder Instruct is 13.1 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 69.3 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 $4.66 per million input tokens, Qwen3-Coder Instruct is above 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
Qwen3-Coder Chat Alibaba Cloud 128000 $4.59 78.4
Llama 3.3 Meta AI 128000 $4.56 78.2
Grok 4 Mini xAI 65536 $12.21 77.9
MiniMax-Text Instruct MiniMax 128000 $4.59 77.7
Qwen3-Coder Instruct Alibaba Cloud 128000 $4.66 78.1
Claude Haiku 4.5 Anthropic 1000000 $13.86 79.4
GPT-5.2 Mini OpenAI 200000 $13.34 80.4

Other benchmarks for Qwen3-Coder Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.5 136 77th
GPQA Diamond 71.7 228 62th
HumanEval 82.2 183 70th
MATH-500 80.5 201 67th
AIME 2025 72.2 216 64th
LiveCodeBench 71.9 149 75th
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 SWE-bench Verified?

Qwen3-Coder Instruct scores 69.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Qwen3-Coder Instruct rank on SWE-bench Verified?

It ranks #126 of 600 models, in the 79th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Qwen3-Coder Instruct 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 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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