Model comparison

DeepSeek-V3.1 vs Qwen3.7 Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 7.7× 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. 1 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Qwen3.7 Flash

Alibaba (Qwen)

39.9

Rank #156 Confirmed

Summary

They share 1 benchmark with published results for both. DeepSeek-V3.1 scores higher in 1 category and Qwen3.7 Flash in 2 categories; one gap is clear of the uncertainty.
The widest gap is in knowledge, where Qwen3.7 Flash leads 48.9 to 43.7.
Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
Qwen3.7 Flash accepts more context: 1M tokens versus 164K.
DeepSeek-V3.1 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1 and Qwen3.7 Flash specifications
	DeepSeek-V3.1	Qwen3.7 Flash
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	42.8	39.9
Released	2025-08-21	2026-07-15
Weights	Open	Proprietary
Context window	164K	1M
Max output	8K	131K
Input $ / M tokens	$0.25	$0.03
Output $ / M tokens	$0.95	$0.13
Results tracked	27	7

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Category by category

Coding Not comparable

DeepSeek-V3.1: 40.3 (#144), Qwen3.7 Flash: —

Coding benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
WeirdML	38.4%	—
LMArena Coding	1417	—

Reasoning Too close to call

DeepSeek-V3.1: 27.9 (#110), Qwen3.7 Flash: 28.2 (#108)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
Epoch Capabilities Index	139.92	144.64
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
NYT Connections (extended)	—	43.8%
Chess Puzzles	—	23%
LMArena Hard Prompts	1417	—
Mystery Game Puzzles	—	15%
DTBench	82.7%	—
LMCA	24.3%	—
ForecastBench	58	—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), Qwen3.7 Flash: 38.3 (#140)

Math benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
FrontierMath (Tiers 1-3)	—	19.3%
OTIS Mock AIME 2024-2025	—	86.7%
LMArena Math	1420	—

Knowledge Qwen3.7 Flash leads

DeepSeek-V3.1: 43.7 (#90), Qwen3.7 Flash: 48.9 (#75)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
GPQA Diamond	—	82.3%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Qwen3.7 Flash: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
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), Qwen3.7 Flash: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Qwen3.7 Flash: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Qwen3.7 Flash: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.7 Flash
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 Qwen3.7 Flash?

Which is cheaper, DeepSeek-V3.1 or Qwen3.7 Flash?

Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Which has the bigger context window?

Qwen3.7 Flash does, with 1M tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Qwen3.7 Flash share?

1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.7 Flash has 7.