Model comparison

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.8 on the Noometry Index. Qwen3.5-9B costs 3.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

Last verified October 10, 2026. 3 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Qwen3.5-9B

Alibaba (Qwen)

33.8

Rank #236 Confirmed

Summary

They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 3 categories and Qwen3.5-9B in 1 category; 4 gaps are clear of the uncertainty.
The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 23.1.
The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 71.2% for Qwen3.5-9B.
Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
Qwen3.5-9B accepts more context: 262K tokens versus 164K.

Side by side

DeepSeek-V3.1 and Qwen3.5-9B specifications
	DeepSeek-V3.1	Qwen3.5-9B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	42.8	33.8
Released	2025-08-21	2026-02-23
Weights	Open	Open
Context window	164K	262K
Max output	8K	66K
Input $ / M tokens	$0.25	$0.10
Output $ / M tokens	$0.95	$0.15
Results tracked	27	10

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Qwen3.5-9B: 35.9 (#217)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
SciCode	—	27.5%
WeirdML	38.4%	—
LMArena Coding	1417	—

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Qwen3.5-9B: 14.5 (#151)

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

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Qwen3.5-9B: 23.1 (#182)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
DTBench	82.7%	71.2%
LMCA	24.3%	24.5%
Epoch Capabilities Index	139.92	139.46
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
CritPt	—	0.3%
Chess Puzzles	—	12%
LMArena Hard Prompts	1417	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Qwen3.5-9B: 34.8 (#192)

Math benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
MathArena Final-Answer Competitions	—	48.5%
OTIS Mock AIME 2024-2025	—	61.7%
LMArena Math	1420	—

Knowledge Qwen3.5-9B leads

DeepSeek-V3.1: 43.7 (#90), Qwen3.5-9B: 46.0 (#84)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
GPQA Diamond	—	79%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Qwen3.5-9B: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
LMArena Non-English	1400	—
LMArena Chinese	1469	—
LMArena French	1447	—
LMArena German	1411	—
LMArena Japanese	1378	—
LMArena Korean	1337	—
LMArena Russian	1405	—
LMArena Spanish	1431	—

Instruction Following Not comparable

DeepSeek-V3.1: 73.9 (#110), Qwen3.5-9B: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Qwen3.5-9B: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Qwen3.5-9B: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.5-9B
LMArena Text	1420	—
LMArena Creative Writing	1401	—
EQ-Bench Creative Writing	1436	—
LMArena Multi-Turn	1408	—

Frequently asked questions

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

Which is cheaper, DeepSeek-V3.1 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 lists at $0.25 and $0.95.

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

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 35.9 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 and Qwen3.5-9B share?

3 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.5-9B has 10.