Model comparison

DeepSeek-V3.1-Terminus vs Qwen3.5-9B

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 33.3 on the Noometry Index. Qwen3.5-9B costs 4.2× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

Last verified October 10, 2026. 4 shared benchmarks.

DeepSeek-V3.1-Terminus

DeepSeek

42.6

Rank #96 Confirmed

Qwen3.5-9B

Alibaba (Qwen)

33.3

Rank #235 Confirmed

Summary

They share 4 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 3 categories and Qwen3.5-9B in 0 categories; 3 gaps are clear of the uncertainty.
The widest gap is in coding, where DeepSeek-V3.1-Terminus leads 41.5 to 35.3.
The biggest single-benchmark swing is SciCode: 40.6% for DeepSeek-V3.1-Terminus and 27.5% for Qwen3.5-9B.
Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.30 / $1 for DeepSeek-V3.1-Terminus.
Qwen3.5-9B accepts more context: 262K tokens versus 164K.

Side by side

DeepSeek-V3.1-Terminus and Qwen3.5-9B specifications
	DeepSeek-V3.1-Terminus	Qwen3.5-9B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	42.6	33.3
Released	2025-09-22	2026-02-23
Weights	Open	Open
Context window	164K	262K
Max output	66K	66K
Input $ / M tokens	$0.30	$0.10
Output $ / M tokens	$1	$0.15
Results tracked	16	10

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 41.5 (#114), Qwen3.5-9B: 35.3 (#213)

Coding benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
SciCode	40.6%	27.5%
LMArena Coding	1426	—
ALE-Bench	745.17	—

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3.5-9B: 14.4 (#143)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
Terminal-Bench	—	9.2%

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 25.7 (#126), Qwen3.5-9B: 22.5 (#178)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
CritPt	1.7%	0.3%
DTBench	81.3%	71.2%
LMCA	28.6%	24.5%
Kagi LLM Benchmark	57.4%	—
Chess Puzzles	—	12%
LMArena Hard Prompts	1427	—
Epoch Capabilities Index	—	139.45

Math DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 35.7 (#138), Qwen3.5-9B: 33.5 (#174)

Math benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
MathArena Final-Answer Competitions	—	48.5%
OTIS Mock AIME 2024-2025	—	61.7%
LMArena Math	1402	—

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3.5-9B: 46.0 (#77)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
GPQA Diamond	—	79%

Multilingual Not comparable

DeepSeek-V3.1-Terminus: 52.3 (#87), Qwen3.5-9B: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
LMArena Non-English	1407	—
LMArena Russian	1434	—

Instruction Following Not comparable

DeepSeek-V3.1-Terminus: 74.4 (#96), Qwen3.5-9B: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
LMArena Instruction Following	1406	—

Long Context Not comparable

DeepSeek-V3.1-Terminus: 43.5 (#88), Qwen3.5-9B: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
LMArena Longer Query	1426	—

Writing & Preference Not comparable

DeepSeek-V3.1-Terminus: 61.3 (#87), Qwen3.5-9B: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1-Terminus	Qwen3.5-9B
LMArena Text	1420	—
LMArena Creative Writing	1404	—
LMArena Multi-Turn	1411	—

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Qwen3.5-9B?

Which is cheaper, DeepSeek-V3.1-Terminus or Qwen3.5-9B?

Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.30 and $1.

Is DeepSeek-V3.1-Terminus or Qwen3.5-9B better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 35.3 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5-9B does, with 262K tokens against 164K.

How many benchmarks do DeepSeek-V3.1-Terminus and Qwen3.5-9B share?

4 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3.5-9B has 10.