Coding · DeepSeek

DeepSeek-V3.2 Instruct on SWE-bench Verified: score, rank and what it means

DeepSeek-V3.2 Instruct scores 75.2 on SWE-bench Verified, placing it #39 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
75.2
0–100 scale
Rank
#39
of 600
Percentile
94th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
6.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with DeepSeek-V3.2 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, DeepSeek-V3.2 Instruct sits at #39 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 6.5 points. The median model in this index scores 61.0, which puts DeepSeek-V3.2 Instruct above the midpoint.

DeepSeek models average 60.0 on this benchmark. DeepSeek-V3.2 Instruct is 15.2 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among frontier models, the average is 75.6. DeepSeek-V3.2 Instruct is 0.4 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 75.2 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 $5.91 per million input tokens, DeepSeek-V3.2 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
o4 Vision OpenAI 200000 $17.01 84.5
Gemini 2.5 Pro Max Google DeepMind 200000 $16.94 84.8
Qwen3-Max Reasoning Alibaba Cloud 2000000 $18.12 85.9
Claude Sonnet 4.5 Turbo Anthropic 1000000 $18.37 85.9
DeepSeek-V3.2 Instruct DeepSeek 128000 $5.91 83.7
Kimi K2 Instruct Moonshot AI 1000000 $5.80 82.3
Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

Other benchmarks for DeepSeek-V3.2 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 84.6 68 89th
GPQA Diamond 80.9 44 93th
HumanEval 90.2 42 93th
MATH-500 86.3 87 86th
AIME 2025 83.9 49 92th
LiveCodeBench 75.2 93 85th
IFEval 91.3 46 92th
TruthfulQA 84.3 78 87th
HellaSwag 88.8 116 81th
MMMU 80.4 53 91th
Arena Elo 1454.4 64 89th

FAQ

What score does DeepSeek-V3.2 Instruct get on SWE-bench Verified?

DeepSeek-V3.2 Instruct scores 75.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does DeepSeek-V3.2 Instruct rank on SWE-bench Verified?

It ranks #39 of 600 models, in the 94th percentile. The leader is GPT-5.2 Pro at 81.7.

Is DeepSeek-V3.2 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 DeepSeek-V3.2 Instruct cost?

DeepSeek-V3.2 Instruct costs $5.91 per million input tokens and $29.01 per million output tokens.

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