Noometry

Model comparison

DeepSeek-V3.1 vs Mixtral 8x22B

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

Last verified . 21 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Mixtral 8x22B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 15.1.
  • The biggest single-benchmark swing is WeirdML: 38.4% for DeepSeek-V3.1 and 3.2% for Mixtral 8x22B.
  • DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • DeepSeek-V3.1 accepts more context: 164K tokens versus 64K.

Side by side

DeepSeek-V3.1Mixtral 8x22B
ProviderDeepSeekMistral AI
Noometry Index42.827.1
Released2025-08-212024-04-17
WeightsOpenOpen
Context window164K64K
Max output8K64K
Input $ / M tokens$0.25$2
Output $ / M tokens$0.95$6
Results tracked2734

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Mixtral 8x22B: 24.2 (#329)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
WeirdML38.4%3.2%
LMArena Coding14171166
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Mixtral 8x22B: 23.1 (#127)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
Cybench—7.5%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Mixtral 8x22B: 19.9 (#248)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Hard Prompts14171150
DTBench82.7%55.1%
Epoch Capabilities Index139.92122.03
ForecastBench5856.3
SimpleBench40%—
Kagi LLM Benchmark53.2%—
LMCA24.3%—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Mixtral 8x22B: 22.9 (#275)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Math14201184
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Mixtral 8x22B: 15.1 (#293)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Expert14051113
GPQA Diamond—34.1%
MMLU-Pro—46%
Vectara Hallucination Rate5.5%—
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Mixtral 8x22B: 32.8 (#255)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Non-English14001128
LMArena Chinese14691116
LMArena French14471166
LMArena German14111141
LMArena Japanese13781037
LMArena Korean13371057
LMArena Russian14051158
LMArena Spanish14311151

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Mixtral 8x22B: 57.7 (#266)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Instruction Following14001147
IFEval—72.4%

Long Context DeepSeek-V3.1 leads

DeepSeek-V3.1: 36.3 (#232), Mixtral 8x22B: 34.7 (#247)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Longer Query14221144
Fiction.LiveBench52.8%—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Mixtral 8x22B: 36.9 (#262)

BenchmarkDeepSeek-V3.1Mixtral 8x22B
LMArena Text14201162
LMArena Creative Writing14011141
LMArena Multi-Turn14081130
EQ-Bench Creative Writing1436—
WildBench—71.1%

Frequently asked questions

Is DeepSeek-V3.1 better than Mixtral 8x22B?

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 8x22B?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is DeepSeek-V3.1 or Mixtral 8x22B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 64K.

How many benchmarks do DeepSeek-V3.1 and Mixtral 8x22B share?

21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mixtral 8x22B has 34.

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