Model comparison

DeepSeek-V2.5 (Sep 2024) vs Qwen3.5 27B

Qwen3.5 27B is the stronger model overall, scoring 41.9 to 37.6 on the Noometry Index.

Last verified . 17 shared benchmarks.

DeepSeek-V2.5 (Sep 2024) DeepSeek

37.6

Rank #200 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

  • They share 17 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 0 categories and Qwen3.5 27B in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where Qwen3.5 27B leads 59.3 to 49.8.

Side by side

DeepSeek-V2.5 (Sep 2024) and Qwen3.5 27B specifications
DeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
ProviderDeepSeekAlibaba (Qwen)
Noometry Index37.641.9
Released2024-09-062026-02-23
WeightsOpenOpen
Context window—262K
Max output—66K
Input $ / M tokens—$0.30
Output $ / M tokens—$2.40
Results tracked2228

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Category by category

Coding Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Qwen3.5 27B: 38.9 (#168)

Coding benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Coding13091427
Aider Polyglot17.8%—
LMArena WebDev—1358
WeirdML—39.5%
BigCodeBench Instruct48.6%—
BigCodeBench Complete53.2%—
ALE-Bench—349.45
HumanEval+83.5%—
MBPP+74.1%—

Agentic & Tool Use Not comparable

DeepSeek-V2.5 (Sep 2024): —, Qwen3.5 27B: —

Agentic & Tool Use benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
Vending-Bench 2—201.98

Reasoning Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Qwen3.5 27B: 27.5 (#117)

Reasoning benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Hard Prompts12891414
NYT Connections (extended)—47.9%
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%

Math Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Qwen3.5 27B: 38.8 (#127)

Math benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Math12881429
MathArena Final-Answer Competitions—56.7%

Knowledge Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Qwen3.5 27B: 38.0 (#150)

Knowledge benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Expert12661428
Vectara Hallucination Rate—12.1%

Multimodal Not comparable

DeepSeek-V2.5 (Sep 2024): —, Qwen3.5 27B: 39.4 (#59)

Multimodal benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Vision—1241

Multilingual Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Qwen3.5 27B: 50.8 (#115)

Multilingual benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Non-English12731390
LMArena Chinese13181478
LMArena French12891410
LMArena German12581393
LMArena Japanese12281345
LMArena Korean12091358
LMArena Russian12891390
LMArena Spanish12481407

Instruction Following Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Qwen3.5 27B: 73.5 (#119)

Instruction Following benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Instruction Following12801393

Long Context Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Qwen3.5 27B: 43.1 (#106)

Long Context benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Longer Query13011413

Writing & Preference Qwen3.5 27B leads

DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Qwen3.5 27B: 59.3 (#111)

Writing & Preference benchmarks
BenchmarkDeepSeek-V2.5 (Sep 2024)Qwen3.5 27B
LMArena Text12941409
LMArena Creative Writing12851362
LMArena Multi-Turn12971410

Frequently asked questions

Is DeepSeek-V2.5 (Sep 2024) better than Qwen3.5 27B?

Qwen3.5 27B is the stronger model overall, scoring 41.9 to 37.6 on the Noometry Index.

Is DeepSeek-V2.5 (Sep 2024) or Qwen3.5 27B better for coding?

Qwen3.5 27B scores higher on coding benchmarks: 38.9 versus 31.7 in the Noometry coding category.

How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3.5 27B share?

17 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3.5 27B has 28.

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