Model comparison

DeepSeek-V3.1-Terminus vs Qwen3.5 27B

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.9 on the Noometry Index.

Last verified October 10, 2026. 13 shared benchmarks.

DeepSeek-V3.1-Terminus

DeepSeek

43.1

Rank #97 Confirmed

Qwen3.5 27B

Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

They share 13 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 5 categories and Qwen3.5 27B in 2 categories; 4 gaps are clear of the uncertainty.
The widest gap is in coding, where DeepSeek-V3.1-Terminus leads 42.0 to 38.9.
The biggest single-benchmark swing is LMCA: 28.6% for DeepSeek-V3.1-Terminus and 34% for Qwen3.5 27B.
DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 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-Terminus and Qwen3.5 27B specifications
	DeepSeek-V3.1-Terminus	Qwen3.5 27B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	43.1	41.9
Released	2025-09-22	2026-02-23
Weights	Open	Open
Context window	164K	262K
Max output	147K	66K
Input $ / M tokens	$0.27	$0.30
Output $ / M tokens	$1	$2.40
Results tracked	16	28

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen3.5 27B: 38.9 (#168)

Coding benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Coding	1426	1427
ALE-Bench	745.17	349.45
LMArena WebDev	—	1358
SciCode	40.6%	—
WeirdML	—	39.5%

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3.5 27B: —

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

Reasoning Qwen3.5 27B leads

DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen3.5 27B: 27.5 (#117)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Hard Prompts	1426	1414
DTBench	81.3%	82.4%
LMCA	28.6%	34%
Kagi LLM Benchmark	57.4%	—
NYT Connections (extended)	—	47.9%
CritPt	1.7%	—
Thematic Generalization	—	45.5%

Math Too close to call

DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen3.5 27B: 38.8 (#127)

Math benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Math	1402	1429
MathArena Final-Answer Competitions	—	56.7%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3.5 27B: 38.0 (#150)

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

Multimodal Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3.5 27B: 39.4 (#59)

Multimodal benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Vision	—	1241

Multilingual DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen3.5 27B: 50.8 (#115)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Non-English	1407	1390
LMArena Russian	1436	1390
LMArena Chinese	—	1478
LMArena French	—	1410
LMArena German	—	1393
LMArena Japanese	—	1345
LMArena Korean	—	1358
LMArena Spanish	—	1407

Instruction Following Too close to call

DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen3.5 27B: 73.5 (#119)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Instruction Following	1404	1393

Long Context Too close to call

DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen3.5 27B: 43.1 (#106)

Long Context benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Longer Query	1421	1413

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen3.5 27B: 59.3 (#111)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5 27B
LMArena Text	1419	1409
LMArena Creative Writing	1403	1362
LMArena Multi-Turn	1411	1410

Frequently asked questions

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

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.9 on the Noometry Index.

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

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.

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

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 38.9 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-Terminus and Qwen3.5 27B share?

13 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3.5 27B has 28.