Model comparison

DeepSeek-V3.1 vs Qwen3.6 Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 38.8 on the Noometry Index.

Last verified October 12, 2026. 4 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Qwen3.6 Flash

Alibaba (Qwen)

38.8

Rank #182 Confirmed

Summary

They share 4 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 1 category and Qwen3.6 Flash in 2 categories; 2 gaps are clear of the uncertainty.
The biggest single-benchmark swing is LMCA: 24.3% for DeepSeek-V3.1 and 31% for Qwen3.6 Flash.
Both cost about the same: $0.25 input and $0.95 output per million tokens.
Qwen3.6 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 Qwen3.6 Flash specifications
	DeepSeek-V3.1	Qwen3.6 Flash
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	42.8	38.8
Released	2025-08-21	2026-04-27
Weights	Open	Proprietary
Context window	164K	1M
Max output	8K	66K
Input $ / M tokens	$0.25	$0.19
Output $ / M tokens	$0.95	$1.13
Results tracked	27	13

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Category by category

Coding Not comparable

DeepSeek-V3.1: 40.3 (#144), Qwen3.6 Flash: —

Coding benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
WeirdML	38.4%	—
LMArena Coding	1417	—
ALE-Bench	—	326.4

Reasoning Qwen3.6 Flash leads

DeepSeek-V3.1: 27.9 (#110), Qwen3.6 Flash: 29.0 (#96)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
SimpleBench	40%	35.2%
DTBench	82.7%	77.1%
LMCA	24.3%	31%
Epoch Capabilities Index	139.92	143.26
Kagi LLM Benchmark	53.2%	—
Chess Puzzles	—	20%
LMArena Hard Prompts	1417	—
Mystery Game Puzzles	—	18%
ForecastBench	58	—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), Qwen3.6 Flash: 39.0 (#117)

Math benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
FrontierMath (Tiers 1-3)	—	22.5%
OTIS Mock AIME 2024-2025	—	84.4%
LMArena Math	1420	—
FrontierMath (Feb 2025 set)	—	10.3%
FrontierMath Tier 4 (v1)	—	0%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Qwen3.6 Flash: 42.1 (#100)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
GPQA Diamond	—	83.3%
SimpleQA Verified	—	15.9%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Qwen3.6 Flash: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 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), Qwen3.6 Flash: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Qwen3.6 Flash: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 Flash
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Qwen3.6 Flash: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Qwen3.6 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 Qwen3.6 Flash?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 38.8 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or Qwen3.6 Flash?

Qwen3.6 Flash is cheaper. It lists at $0.19 per million input tokens and $1.13 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Which has the bigger context window?

Qwen3.6 Flash does, with 1M tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Qwen3.6 Flash share?

4 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.6 Flash has 13.