Model comparison

DeepSeek-V3.1 vs Mistral Small 3.2

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 3.2× 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

Mistral Small 3.2

Mistral AI

31.2

Rank #280 Confirmed

Summary

They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 26.7.
The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 40.4% for Mistral Small 3.2.
Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
Mistral Small 3.2 accepts more context: 256K tokens versus 164K.

Side by side

DeepSeek-V3.1 and Mistral Small 3.2 specifications
	DeepSeek-V3.1	Mistral Small 3.2
Provider	DeepSeek	Mistral AI
Noometry Index	42.8	31.2
Released	2025-08-21	2025-06-20
Weights	Open	Open
Context window	164K	256K
Max output	8K	16K
Input $ / M tokens	$0.25	$0.0938
Output $ / M tokens	$0.95	$0.25
Results tracked	27	6

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Category by category

Coding Not comparable

DeepSeek-V3.1: 40.3 (#144), Mistral Small 3.2: —

Coding benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
WeirdML	38.4%	—
LMArena Coding	1417	—

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Mistral Small 3.2: 18.1 (#287)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
Kagi LLM Benchmark	53.2%	40.4%
Epoch Capabilities Index	139.92	131.74
SimpleBench	40%	—
Chess Puzzles	—	1%
LMArena Hard Prompts	1417	—
DTBench	82.7%	—
LMCA	24.3%	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Mistral Small 3.2: 26.3 (#260)

Math benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
OTIS Mock AIME 2024-2025	—	30.3%
LMArena Math	1420	—

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Mistral Small 3.2: 26.7 (#256)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
GPQA Diamond	—	49.1%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Mistral Small 3.2: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
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), Mistral Small 3.2: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Mistral Small 3.2: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Mistral Small 3.2: 45.0 (#224)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Mistral Small 3.2
EQ-Bench Creative Writing	1436	1255
LMArena Text	1420	—
LMArena Creative Writing	1401	—
LMArena Multi-Turn	1408	—

Frequently asked questions

Is DeepSeek-V3.1 better than Mistral Small 3.2?

Which is cheaper, DeepSeek-V3.1 or Mistral Small 3.2?

Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Which has the bigger context window?

Mistral Small 3.2 does, with 256K tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Mistral Small 3.2 share?

3 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Small 3.2 has 6.