Model comparison

DeepSeek-V3.1 vs Qwen3.5 27B

DeepSeek-V3.1 and Qwen3.5 27B score almost the same on the Noometry Index (42.2 vs 41.4), so choose on price, context window or the category you care about most.

Last verified October 10, 2026. 21 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.2

Rank #106 Confirmed

Qwen3.5 27B

Alibaba (Qwen)

41.4

Rank #127 Confirmed

Summary

They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and Qwen3.5 27B in 2 categories; 3 gaps are clear of the uncertainty.
The widest gap is in long context, where Qwen3.5 27B leads 43.1 to 36.3.
The biggest single-benchmark swing is LMCA: 24.3% for DeepSeek-V3.1 and 34% for Qwen3.5 27B.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
Qwen3.5 27B accepts more context: 262K tokens versus 164K.

Side by side

DeepSeek-V3.1 and Qwen3.5 27B specifications
	DeepSeek-V3.1	Qwen3.5 27B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	42.2	41.4
Released	2025-08-21	2026-02-23
Weights	Open	Open
Context window	164K	262K
Max output	8K	66K
Input $ / M tokens	$0.25	$0.30
Output $ / M tokens	$0.95	$2.40
Results tracked	27	28

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 39.8 (#140), Qwen3.5 27B: 38.6 (#159)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
WeirdML	38.4%	39.5%
LMArena Coding	1417	1427
LMArena WebDev	—	1357
ALE-Bench	—	349.45

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Qwen3.5 27B: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
Vending-Bench 2	—	201.98

Reasoning Too close to call

DeepSeek-V3.1: 27.3 (#109), Qwen3.5 27B: 26.6 (#116)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Hard Prompts	1417	1414
DTBench	82.7%	82.4%
LMCA	24.3%	34%
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
NYT Connections (extended)	—	47.9%
Thematic Generalization	—	45.5%
Epoch Capabilities Index	139.92	—
ForecastBench	58	—

Math Too close to call

DeepSeek-V3.1: 36.1 (#125), Qwen3.5 27B: 36.8 (#103)

Math benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Math	1420	1428
MathArena Final-Answer Competitions	—	56.7%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.0 (#86), Qwen3.5 27B: 37.4 (#149)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
Vectara Hallucination Rate	5.5%	12.1%
LMArena Expert	1403	1428

Multimodal Not comparable

DeepSeek-V3.1: —, Qwen3.5 27B: 38.7 (#56)

Multimodal benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Vision	—	1240

Multilingual Too close to call

DeepSeek-V3.1: 51.8 (#100), Qwen3.5 27B: 51.0 (#112)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Non-English	1400	1390
LMArena Chinese	1470	1478
LMArena French	1447	1409
LMArena German	1412	1393
LMArena Japanese	1374	1345
LMArena Korean	1337	1359
LMArena Russian	1405	1389
LMArena Spanish	1429	1408

Instruction Following Too close to call

DeepSeek-V3.1: 74.1 (#105), Qwen3.5 27B: 73.7 (#114)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Instruction Following	1400	1393

Long Context Qwen3.5 27B leads

DeepSeek-V3.1: 36.3 (#227), Qwen3.5 27B: 43.1 (#103)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Longer Query	1422	1413
Fiction.LiveBench	52.8%	—

Writing & Preference Too close to call

DeepSeek-V3.1: 60.5 (#93), Qwen3.5 27B: 59.5 (#106)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5 27B
LMArena Text	1420	1409
LMArena Creative Writing	1401	1362
LMArena Multi-Turn	1408	1410
EQ-Bench Creative Writing	1436	—

Frequently asked questions

Is DeepSeek-V3.1 better than Qwen3.5 27B?

DeepSeek-V3.1 and Qwen3.5 27B score almost the same on the Noometry Index (42.2 vs 41.4), so choose on price, context window or the category you care about most.

Which is cheaper, DeepSeek-V3.1 or Qwen3.5 27B?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.

Is DeepSeek-V3.1 or Qwen3.5 27B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 38.6 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 27B does, with 262K tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Qwen3.5 27B share?

21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.5 27B has 28.