Noometry

Model comparison

DeepSeek-V3.1 vs Mixtral 8x7B

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 27.1 on the Noometry Index.

Last verified . 20 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 11.0.
  • The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 49.6% for Mixtral 8x7B.
  • DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
  • DeepSeek-V3.1 accepts more context: 164K tokens versus 32K.

Side by side

DeepSeek-V3.1Mixtral 8x7B
ProviderDeepSeekMistral AI
Noometry Index42.827.1
Released2025-08-212023-12-11
WeightsOpenOpen
Context window164K32K
Max output8K32K
Input $ / M tokens$0.25$0.70
Output $ / M tokens$0.95$0.70
Results tracked2738

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Mixtral 8x7B: 32.8 (#269)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Coding14171126
WeirdML38.4%—
HumanEval+—39.6%
MBPP+—49.7%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Mixtral 8x7B: 18.2 (#285)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Hard Prompts14171115
DTBench82.7%49.6%
Epoch Capabilities Index139.92118.47
ForecastBench5856.3
SimpleBench40%—
Kagi LLM Benchmark53.2%—
LMCA24.3%—
Adversarial NLI—55.2%
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Mixtral 8x7B: 18.8 (#289)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Math14201147
Omni-MATH—10.5%
MATH Level 5—10%
GSM8K—74.4%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Mixtral 8x7B: 11.0 (#301)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Expert14051088
GPQA Diamond—30.6%
MMLU-Pro—33.5%
Vectara Hallucination Rate5.5%—
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Mixtral 8x7B: 29.6 (#266)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Non-English14001077
LMArena Chinese14691055
LMArena French14471166
LMArena German14111114
LMArena Japanese1378931
LMArena Korean1337968
LMArena Russian14051090
LMArena Spanish14311111

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Mixtral 8x7B: 51.0 (#297)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Instruction Following14001109
IFEval—57.5%

Long Context DeepSeek-V3.1 leads

DeepSeek-V3.1: 36.3 (#232), Mixtral 8x7B: 33.4 (#260)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Longer Query14221103
Fiction.LiveBench52.8%—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Mixtral 8x7B: 34.2 (#270)

BenchmarkDeepSeek-V3.1Mixtral 8x7B
LMArena Text14201132
LMArena Creative Writing14011109
LMArena Multi-Turn14081115
EQ-Bench Creative Writing1436—
WildBench—67.3%

Frequently asked questions

Is DeepSeek-V3.1 better than Mixtral 8x7B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 27.1 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or Mixtral 8x7B?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

Is DeepSeek-V3.1 or Mixtral 8x7B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 32K.

How many benchmarks do DeepSeek-V3.1 and Mixtral 8x7B share?

20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mixtral 8x7B has 38.

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