Model comparison

DeepSeek-V3.1 vs Mistral Large

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index.

Last verified October 10, 2026. 24 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Mistral Large

Mistral AI

31.9

Rank #263 Confirmed

Summary

They share 24 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Mistral Large in 1 category; 8 gaps are clear of the uncertainty.
The widest gap is in math, where DeepSeek-V3.1 leads 38.9 to 18.2.
The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 65.1% for Mistral Large.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $2 / $6 for Mistral Large.
DeepSeek-V3.1 accepts more context: 164K tokens versus 131K.

Side by side

DeepSeek-V3.1 and Mistral Large specifications
	DeepSeek-V3.1	Mistral Large
Provider	DeepSeek	Mistral AI
Noometry Index	42.8	31.9
Released	2025-08-21	2024-02-26
Weights	Open	Open
Context window	164K	131K
Max output	8K	16K
Input $ / M tokens	$0.25	$2
Output $ / M tokens	$0.95	$6
Results tracked	27	51

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Mistral Large: 34.3 (#240)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Coding	1417	1277
SciCode	—	36.2%
WeirdML	38.4%	—
BigCodeBench Instruct	—	30%
LiveBench Coding	—	47.1%
BigCodeBench Complete	—	38.3%
ALE-Bench	—	264.7
HumanEval+	—	62.2%
MBPP+	—	59.5%

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Mistral Large: 28.6 (#89)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
Berkeley Function Calling Leaderboard	—	38.4%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Mistral Large: 15.8 (#310)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
SimpleBench	40%	22.5%
LMArena Hard Prompts	1417	1257
DTBench	82.7%	65.1%
LMCA	24.3%	16.7%
Epoch Capabilities Index	139.92	128.52
ForecastBench	58	57.1
Kagi LLM Benchmark	53.2%	—
CritPt	—	0%
LiveBench Reasoning	—	43.5%
LiveBench Data Analysis	—	50.1%
LiveBench	—	48.4%

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Mistral Large: 18.2 (#291)

Math benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Math	1420	1262
OTIS Mock AIME 2024-2025	—	8.5%
Omni-MATH	—	28.1%
LiveBench Math	—	42.5%
MATH Level 5	—	50.3%
FrontierMath (Feb 2025 set)	—	0.3%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Mistral Large: 30.1 (#230)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
Vectara Hallucination Rate	5.5%	4.5%
LMArena Expert	1405	1232
GPQA Diamond	—	51.3%
MMLU-Pro	—	59.9%
Confabulations	—	21.4%
GPQA (HELM)	—	43.5%
MMLU	—	80%

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Mistral Large: 40.0 (#219)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Non-English	1400	1237
LMArena Chinese	1469	1240
LMArena French	1447	1325
LMArena German	1411	1254
LMArena Japanese	1378	1188
LMArena Korean	1337	1202
LMArena Russian	1405	1257
LMArena Spanish	1431	1268

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Mistral Large: 67.9 (#191)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Instruction Following	1400	1249
LiveBench Instruction Following	—	67.9%
IFEval	—	87.7%

Long Context Mistral Large leads

DeepSeek-V3.1: 36.3 (#232), Mistral Large: 38.3 (#199)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Longer Query	1422	1261
Fiction.LiveBench	52.8%	—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Mistral Large: 40.7 (#242)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Mistral Large
LMArena Text	1420	1266
LMArena Creative Writing	1401	1243
EQ-Bench Creative Writing	1436	985
LMArena Multi-Turn	1408	1260
Short-Story Creative Writing	—	69%
WildBench	—	80.1%
LiveBench Language	—	39.4%

Frequently asked questions

Is DeepSeek-V3.1 better than Mistral Large?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.9 on the Noometry Index.

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

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Mistral Large lists at $2 and $6.

Is DeepSeek-V3.1 or Mistral Large better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 34.3 in the Noometry coding category.

Which has the bigger context window?

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

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

24 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Large has 51.