Model comparison

DeepSeek-V3.1 vs Mistral Nemo

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 25.9 on the Noometry Index. Mistral Nemo costs 2.8× 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

42.2

Rank #106 Confirmed

Mistral Nemo

Mistral AI

25.9

Rank #332 Confirmed

Summary

They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Mistral Nemo in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 28.6.
The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 48.6% for Mistral Nemo.
Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.1 and Mistral Nemo specifications
	DeepSeek-V3.1	Mistral Nemo
Provider	DeepSeek	Mistral AI
Noometry Index	42.2	25.9
Released	2025-08-21	2024-07-01
Weights	Open	Open
Context window	164K	128K
Max output	8K	128K
Input $ / M tokens	$0.25	$0.15
Output $ / M tokens	$0.95	$0.15
Results tracked	27	10

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Category by category

Coding Not comparable

DeepSeek-V3.1: 39.8 (#140), Mistral Nemo: —

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

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Mistral Nemo: 23.3 (#118)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
Berkeley Function Calling Leaderboard	—	27.6%
BALROG	—	17.6%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.3 (#109), Mistral Nemo: 19.9 (#230)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
DTBench	82.7%	48.6%
Epoch Capabilities Index	139.92	118.63
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
LMArena Hard Prompts	1417	—
LMCA	24.3%	—
ForecastBench	58	—
PIQA	—	83.5%

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 36.1 (#125), Mistral Nemo: 23.5 (#263)

Math benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
LMArena Math	1420	—
MATH Level 5	—	10.8%
GSM8K	—	84.2%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.0 (#86), Mistral Nemo: 12.3 (#293)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
GPQA Diamond	—	29.9%
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1403	—
BoolQ	—	82.5%

Multilingual Not comparable

DeepSeek-V3.1: 51.8 (#100), Mistral Nemo: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
LMArena Non-English	1400	—
LMArena Chinese	1470	—
LMArena French	1447	—
LMArena German	1412	—
LMArena Japanese	1374	—
LMArena Korean	1337	—
LMArena Russian	1405	—
LMArena Spanish	1429	—

Instruction Following Not comparable

DeepSeek-V3.1: 74.1 (#105), Mistral Nemo: —

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

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#227), Mistral Nemo: —

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

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.5 (#93), Mistral Nemo: 28.6 (#291)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Mistral Nemo
EQ-Bench Creative Writing	1436	881
LMArena Text	1420	—
LMArena Creative Writing	1401	—
LMArena Multi-Turn	1408	—

Frequently asked questions

Is DeepSeek-V3.1 better than Mistral Nemo?

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

Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 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 128K.

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

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