Model comparison

DeepSeek-V3.2-Speciale vs Qwen2.5-Coder-32B

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.4 on the Noometry Index.

Last verified October 11, 2026. 0 shared benchmarks.

DeepSeek-V3.2-Speciale

DeepSeek

39.7

Rank #162 Reported

Qwen2.5-Coder-32B

Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

The widest gap is in coding, where DeepSeek-V3.2-Speciale leads 40.4 to 22.6.
Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 33K.

Side by side

DeepSeek-V3.2-Speciale and Qwen2.5-Coder-32B specifications
	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	39.7	33.4
Released	2025-12-01	2024-09-18
Weights	Open	Open
Context window	128K	33K
Max output	128K	29K
Input $ / M tokens	$0.58	$0.66
Output $ / M tokens	$1.68	$1
Results tracked	3	31

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Category by category

Coding DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 40.4 (#140), Qwen2.5-Coder-32B: 22.6 (#333)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
SWE-bench Verified (bash only)	—	9%
Aider Polyglot	—	16.4%
WeirdML	46.7%	—
BigCodeBench Instruct	—	49%
LiveBench Coding	—	56.9%
LMArena Coding	—	1276
BigCodeBench Complete	—	58%
HumanEval+	—	87.2%
MBPP+	—	77%

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.9 (#73), Qwen2.5-Coder-32B: 21.2 (#225)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
SimpleBench	52.6%	—
LiveBench Reasoning	—	42.1%
LMArena Hard Prompts	—	1251
LiveBench Data Analysis	—	49.9%
Epoch Capabilities Index	—	119.49
HellaSwag	—	83%
LiveBench	—	46.2%
WinoGrande	—	80.8%

Math Not comparable

DeepSeek-V3.2-Speciale: —, Qwen2.5-Coder-32B: 33.3 (#204)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LiveBench Math	—	46.6%
LMArena Math	—	1251
GSM8K	—	93%

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, Qwen2.5-Coder-32B: 33.4 (#203)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LMArena Expert	—	1221
ARC (AI2) Challenge	—	70.5%
MMLU	—	79.1%

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, Qwen2.5-Coder-32B: 37.8 (#235)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LMArena Non-English	—	1205
LMArena Chinese	—	1222
LMArena Russian	—	1228

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, Qwen2.5-Coder-32B: 61.4 (#245)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LiveBench Instruction Following	—	58.7%
LMArena Instruction Following	—	1223

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, Qwen2.5-Coder-32B: 38.0 (#208)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LMArena Longer Query	—	1251

Writing & Preference DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 46.0 (#222), Qwen2.5-Coder-32B: 41.6 (#240)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Qwen2.5-Coder-32B
LMArena Text	—	1230
LMArena Creative Writing	—	1174
EQ-Bench Creative Writing	1276	—
LMArena Multi-Turn	—	1222
LiveBench Language	—	23.3%

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than Qwen2.5-Coder-32B?

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.4 on the Noometry Index.

Which is cheaper, DeepSeek-V3.2-Speciale or Qwen2.5-Coder-32B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

Is DeepSeek-V3.2-Speciale or Qwen2.5-Coder-32B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.2-Speciale does, with 128K tokens against 33K.

How many benchmarks do DeepSeek-V3.2-Speciale and Qwen2.5-Coder-32B share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Qwen2.5-Coder-32B has 31.