Model comparison

DeepSeek-V3.1 vs Ministral 3B

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 26.2 on the Noometry Index. Ministral 3B costs 4.3× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

Last verified October 12, 2026. 4 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Ministral 3B

Mistral AI

26.2

Rank #338 Confirmed

Summary

They share 4 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 3 categories and Ministral 3B in 0 categories; 3 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 10.4.
The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 51.7% for Ministral 3B.
Ministral 3B is cheaper at $0.10 / $0.10 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
DeepSeek-V3.1 accepts more context: 164K tokens versus 131K.

Side by side

DeepSeek-V3.1 and Ministral 3B specifications
	DeepSeek-V3.1	Ministral 3B
Provider	DeepSeek	Mistral AI
Noometry Index	42.8	26.2
Released	2025-08-21	2024-10-01
Weights	Open	Open
Context window	164K	131K
Max output	8K	262K
Input $ / M tokens	$0.25	$0.10
Output $ / M tokens	$0.95	$0.10
Results tracked	27	6

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Category by category

Coding Not comparable

DeepSeek-V3.1: 40.3 (#144), Ministral 3B: —

Coding benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
WeirdML	38.4%	—
LMArena Coding	1417	—

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Ministral 3B: 18.4 (#282)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
DTBench	82.7%	51.7%
LMCA	24.3%	5.5%
Epoch Capabilities Index	139.92	118.1
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
LMArena Hard Prompts	1417	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Ministral 3B: 26.6 (#258)

Math benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
LMArena Math	1420	—
MATH Level 5	—	14.4%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Ministral 3B: 10.4 (#302)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
Vectara Hallucination Rate	5.5%	7.3%
GPQA Diamond	—	25.3%
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Ministral 3B: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
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), Ministral 3B: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Ministral 3B: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Ministral 3B: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Ministral 3B
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 Ministral 3B?

Which is cheaper, DeepSeek-V3.1 or Ministral 3B?

Ministral 3B is cheaper. It lists at $0.10 per million input tokens and $0.10 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 131K.

How many benchmarks do DeepSeek-V3.1 and Ministral 3B share?

4 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Ministral 3B has 6.