Model comparison

DeepSeek-V3 vs Qwen2.5 32B Instruct

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 30.1 on the Noometry Index.

Last verified October 10, 2026. 6 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

Qwen2.5 32B Instruct

Alibaba (Qwen)

30.1

Rank #297 Confirmed

Summary

They share 6 benchmarks with published results for both. DeepSeek-V3 scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in math, where DeepSeek-V3 leads 32.1 to 16.2.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 7.4% for Qwen2.5 32B Instruct.
DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
DeepSeek-V3 accepts more context: 164K tokens versus 131K.

Side by side

DeepSeek-V3 and Qwen2.5 32B Instruct specifications
	DeepSeek-V3	Qwen2.5 32B Instruct
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	39.5	30.1
Released	2024-12-26	2024-09
Weights	Open	Open
Context window	164K	131K
Max output	164K	8K
Input $ / M tokens	$0.24	$0.70
Output $ / M tokens	$0.90	$2.80
Results tracked	60	7

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Category by category

Coding DeepSeek-V3 leads

DeepSeek-V3: 42.3 (#106), Qwen2.5 32B Instruct: 38.7 (#169)

Coding benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
BigCodeBench Instruct	50%	45%
BigCodeBench Complete	62.2%	52.3%
Aider Polyglot	55.1%	—
SciCode	35.8%	—
WeirdML	36.1%	—
LiveBench Coding	70.9%	—
LMArena Coding	1368	—
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, Qwen2.5 32B Instruct: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
METR Time Horizons	49.6%	—

Reasoning DeepSeek-V3 leads

DeepSeek-V3: 20.5 (#236), Qwen2.5 32B Instruct: 19.2 (#266)

Reasoning benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
Epoch Capabilities Index	135.94	128.52
SimpleBench	27.2%	—
Kagi LLM Benchmark	52.3%	—
CritPt	0%	—
Chess Puzzles	—	0%
LiveBench Reasoning	65.8%	—
LMArena Hard Prompts	1365	—
DTBench	64.8%	—
LiveBench Data Analysis	60.9%	—
LMCA	15.5%	—
BIG-Bench Hard	87.5%	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math DeepSeek-V3 leads

DeepSeek-V3: 32.1 (#219), Qwen2.5 32B Instruct: 16.2 (#296)

Math benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
OTIS Mock AIME 2024-2025	37.8%	7.4%
MATH Level 5	75.5%	56.1%
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
LMArena Math	1373	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge DeepSeek-V3 leads

DeepSeek-V3: 37.5 (#155), Qwen2.5 32B Instruct: 24.9 (#266)

Knowledge benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
GPQA Diamond	67.6%	46.1%
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
Vectara Hallucination Rate	6.1%	—
GPQA (HELM)	53.8%	—
LMArena Expert	1351	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multilingual Not comparable

DeepSeek-V3: 48.5 (#143), Qwen2.5 32B Instruct: —

Multilingual benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
LMArena Non-English	1358	—
LMArena Chinese	1391	—
LMArena French	1385	—
LMArena German	1374	—
LMArena Japanese	1333	—
LMArena Korean	1319	—
LMArena Russian	1373	—
LMArena Spanish	1358	—

Instruction Following Not comparable

DeepSeek-V3: 72.8 (#130), Qwen2.5 32B Instruct: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—
LMArena Instruction Following	1345	—

Long Context Not comparable

DeepSeek-V3: 34.0 (#253), Qwen2.5 32B Instruct: —

Long Context benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
Fiction.LiveBench	50%	—
LMArena Longer Query	1352	—

Writing & Preference Not comparable

DeepSeek-V3: 57.4 (#130), Qwen2.5 32B Instruct: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	Qwen2.5 32B Instruct
LMArena Text	1375	—
LMArena Creative Writing	1364	—
Short-Story Creative Writing	77%	—
EQ-Bench Creative Writing	1472	—
WildBench	83%	—
LMArena Multi-Turn	1389	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than Qwen2.5 32B Instruct?

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 30.1 on the Noometry Index.

Which is cheaper, DeepSeek-V3 or Qwen2.5 32B Instruct?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.

Is DeepSeek-V3 or Qwen2.5 32B Instruct better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 38.7 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3 does, with 164K tokens against 131K.

How many benchmarks do DeepSeek-V3 and Qwen2.5 32B Instruct share?

6 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Qwen2.5 32B Instruct has 7.