Coding · Alibaba Cloud

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

QwQ Chat scores 68.3 on SWE-bench Verified, placing it #139 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
68.3
0–100 scale
Rank
#139
of 600
Percentile
77th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
13.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with QwQ Chat 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, QwQ Chat sits at #139 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 13.4 points. The median model in this index scores 61.0, which puts QwQ Chat above the midpoint.

Alibaba Cloud models average 60.8 on this benchmark. QwQ Chat is 7.5 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among multimodal models, the average is 66.4. QwQ Chat is 1.9 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 68.3 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 $4.28 per million input tokens, QwQ Chat 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
Claude Haiku 4.5 Turbo Anthropic 200000 $14.37 80.0
DeepSeek-Coder DeepSeek 32768 $4.55 78.1
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
QwQ Alibaba Cloud 128000 $4.57 78.6
QwQ Chat Alibaba Cloud 65536 $4.28 78.2
ERNIE 4.5 Baidu 65536 $13.44 80.0
Kimi K1.5 Moonshot AI 128000 $5.19 80.7

Other benchmarks for QwQ Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.6 167 72th
GPQA Diamond 72.8 203 66th
HumanEval 83.4 151 75th
MATH-500 82.1 166 72th
AIME 2025 76.1 140 77th
LiveCodeBench 68.8 213 65th
IFEval 85.2 186 69th
TruthfulQA 79.8 191 68th
HellaSwag 87.9 147 76th
MMMU 75.7 145 76th
Arena Elo 1414.1 173 71th

FAQ

What score does QwQ Chat get on SWE-bench Verified?

QwQ Chat scores 68.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does QwQ Chat rank on SWE-bench Verified?

It ranks #139 of 600 models, in the 77th percentile. The leader is GPT-5.2 Pro at 81.7.

Is QwQ Chat 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 QwQ Chat cost?

QwQ Chat costs $4.28 per million input tokens and $16.00 per million output tokens.

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