Model comparison

DeepSeek-V3.1 vs Magistral Small

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 30.2 on the Noometry Index.

Last verified October 11, 2026. 3 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Magistral Small

Mistral AI

30.2

Rank #296 Confirmed

Summary

They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Magistral Small in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 6.8.
The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 6.3% for Magistral Small.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.50 / $1.50 for Magistral Small.
DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.1 and Magistral Small specifications
	DeepSeek-V3.1	Magistral Small
Provider	DeepSeek	Mistral AI
Noometry Index	42.8	30.2
Released	2025-08-21	2025-06-10
Weights	Open	Open
Context window	164K	128K
Max output	8K	40K
Input $ / M tokens	$0.25	$0.50
Output $ / M tokens	$0.95	$1.50
Results tracked	27	10

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Magistral Small: 38.4 (#176)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Magistral Small
SciCode	—	35.2%
WeirdML	38.4%	—
LMArena Coding	1417	—

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Magistral Small: 6.8 (#350)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Magistral Small
Kagi LLM Benchmark	53.2%	6.3%
DTBench	82.7%	61.3%
Epoch Capabilities Index	139.92	133.19
ARC-AGI-2	—	0%
SimpleBench	40%	—
ARC-AGI-1	—	5%
CritPt	—	0.3%
Chess Puzzles	—	3%
LMArena Hard Prompts	1417	—
LMCA	24.3%	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Magistral Small: 26.2 (#261)

Math benchmarks
Benchmark	DeepSeek-V3.1	Magistral Small
OTIS Mock AIME 2024-2025	—	30%
LMArena Math	1420	—

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Magistral Small: 30.9 (#223)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Magistral Small
GPQA Diamond	—	56.1%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

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

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

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

Long Context Not comparable

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

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

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Magistral Small: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Magistral Small
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 Magistral Small?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 30.2 on the Noometry Index.

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

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Magistral Small lists at $0.50 and $1.50.

Is DeepSeek-V3.1 or Magistral Small better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 38.4 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

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

3 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Magistral Small has 10.