Noometry

Model comparison

DeepSeek-V3.1 vs Mistral Small 3.1

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.7 on the Noometry Index.

Last verified . 19 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

Mistral Small 3.1 Mistral AI

31.7

Rank #269 Confirmed

Summary

  • They share 19 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Mistral Small 3.1 in 1 category; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where DeepSeek-V3.1 leads 38.9 to 14.7.
  • Mistral Small 3.1 is cheaper at $0.35 / $0.56 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-V3.1Mistral Small 3.1
ProviderDeepSeekMistral AI
Noometry Index42.831.7
Released2025-08-212025-03-17
WeightsOpenOpen
Context window164K128K
Max output8K102K
Input $ / M tokens$0.25$0.35
Output $ / M tokens$0.95$0.56
Results tracked2728

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Mistral Small 3.1: 38.3 (#179)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Coding14171309
WeirdML38.4%—

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Mistral Small 3.1: 19.7 (#254)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Hard Prompts14171278
Epoch Capabilities Index139.92127.48
SimpleBench40%—
Kagi LLM Benchmark53.2%—
Chess Puzzles—1%
DTBench82.7%—
LMCA24.3%—
ForecastBench58—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Mistral Small 3.1: 14.7 (#301)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Math14201262
OTIS Mock AIME 2024-2025—3.9%
Omni-MATH—24.8%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Mistral Small 3.1: 22.6 (#271)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Expert14051257
GPQA Diamond—41.9%
MMLU-Pro—61%
Vectara Hallucination Rate5.5%—
GPQA (HELM)—39.2%

Multimodal Not comparable

DeepSeek-V3.1: —, Mistral Small 3.1: 33.2 (#99)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Vision—1136

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Mistral Small 3.1: 41.2 (#209)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Non-English14001255
LMArena Chinese14691253
LMArena French14471273
LMArena German14111266
LMArena Japanese13781208
LMArena Korean13371206
LMArena Russian14051263
LMArena Spanish14311283

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Mistral Small 3.1: 63.6 (#230)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Instruction Following14001264
IFEval—75%

Long Context Mistral Small 3.1 leads

DeepSeek-V3.1: 36.3 (#232), Mistral Small 3.1: 39.5 (#178)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Longer Query14221299
Fiction.LiveBench52.8%—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Mistral Small 3.1: 37.0 (#259)

BenchmarkDeepSeek-V3.1Mistral Small 3.1
LMArena Text14201277
LMArena Creative Writing14011253
EQ-Bench Creative Writing1436761
LMArena Multi-Turn14081270
WildBench—78.8%

Frequently asked questions

Is DeepSeek-V3.1 better than Mistral Small 3.1?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 31.7 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or Mistral Small 3.1?

Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Is DeepSeek-V3.1 or Mistral Small 3.1 better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 38.3 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 Mistral Small 3.1 share?

19 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Small 3.1 has 28.

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