Model comparison

Mixtral 8x22B vs Qwen3 32B

Qwen3 32B is the stronger model overall, scoring 39.2 to 27.1 on the Noometry Index.

Last verified . 16 shared benchmarks.

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Qwen3 32B Alibaba (Qwen)

39.2

Rank #172 Confirmed

Summary

  • They share 16 benchmarks with published results for both. Mixtral 8x22B scores higher in 0 categories and Qwen3 32B in 9 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Qwen3 32B leads 40.0 to 15.1.
  • The biggest single-benchmark swing is GPQA Diamond: 34.1% for Mixtral 8x22B and 65.7% for Qwen3 32B.
  • Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • Qwen3 32B accepts more context: 131K tokens versus 64K.

Side by side

Mixtral 8x22B and Qwen3 32B specifications
Mixtral 8x22BQwen3 32B
ProviderMistral AIAlibaba (Qwen)
Noometry Index27.139.2
Released2024-04-172025-04
WeightsOpenOpen
Context window64K131K
Max output64K16K
Input $ / M tokens$2$0.70
Output $ / M tokens$6$2.80
Results tracked3426

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Category by category

Coding Qwen3 32B leads

Mixtral 8x22B: 24.2 (#329), Qwen3 32B: 37.7 (#190)

Coding benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Coding11661358
Aider Polyglot—40%
SciCode—35.4%
WeirdML3.2%—
BigCodeBench Instruct40.6%—
BigCodeBench Complete50.2%—
HumanEval+72%—
MBPP+64.3%—

Agentic & Tool Use Qwen3 32B leads

Mixtral 8x22B: 23.1 (#127), Qwen3 32B: 32.6 (#62)

Agentic & Tool Use benchmarks
BenchmarkMixtral 8x22BQwen3 32B
Berkeley Function Calling Leaderboard—48.7%
Cybench7.5%—

Reasoning Too close to call

Mixtral 8x22B: 19.9 (#248), Qwen3 32B: 20.2 (#241)

Reasoning benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Hard Prompts11501334
DTBench55.1%67.5%
Epoch Capabilities Index122.03138.51
Kagi LLM Benchmark—54.9%
CritPt—0.3%
Chess Puzzles—5%
LMCA—17.3%
ForecastBench56.3—

Math Qwen3 32B leads

Mixtral 8x22B: 22.9 (#275), Qwen3 32B: 39.7 (#99)

Math benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Math11841399
OTIS Mock AIME 2024-2025—66.9%
Omni-MATH16.3%—
MATH Level 524.2%—

Knowledge Qwen3 32B leads

Mixtral 8x22B: 15.1 (#293), Qwen3 32B: 40.0 (#125)

Knowledge benchmarks
BenchmarkMixtral 8x22BQwen3 32B
GPQA Diamond34.1%65.7%
LMArena Expert11131362
MMLU-Pro46%—
Vectara Hallucination Rate—5.9%
GPQA (HELM)33.4%—
MMLU77.8%—

Multilingual Qwen3 32B leads

Mixtral 8x22B: 32.8 (#255), Qwen3 32B: 45.6 (#167)

Multilingual benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Non-English11281317
LMArena Chinese11161357
LMArena German11411341
LMArena Russian11581311
LMArena French1166—
LMArena Japanese1037—
LMArena Korean1057—
LMArena Spanish1151—

Instruction Following Qwen3 32B leads

Mixtral 8x22B: 57.7 (#266), Qwen3 32B: 68.9 (#179)

Instruction Following benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Instruction Following11471305
IFEval72.4%—

Long Context Qwen3 32B leads

Mixtral 8x22B: 34.7 (#247), Qwen3 32B: 43.8 (#87)

Long Context benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Longer Query11441327
Fiction.LiveBench—74.2%

Writing & Preference Qwen3 32B leads

Mixtral 8x22B: 36.9 (#262), Qwen3 32B: 52.9 (#163)

Writing & Preference benchmarks
BenchmarkMixtral 8x22BQwen3 32B
LMArena Text11621340
LMArena Creative Writing11411297
LMArena Multi-Turn11301331
WildBench71.1%—

Frequently asked questions

Is Mixtral 8x22B better than Qwen3 32B?

Qwen3 32B is the stronger model overall, scoring 39.2 to 27.1 on the Noometry Index.

Which is cheaper, Mixtral 8x22B or Qwen3 32B?

Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is Mixtral 8x22B or Qwen3 32B better for coding?

Qwen3 32B scores higher on coding benchmarks: 37.7 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

Qwen3 32B does, with 131K tokens against 64K.

How many benchmarks do Mixtral 8x22B and Qwen3 32B share?

16 benchmarks have published results for both models. Mixtral 8x22B has 34 scored results on Noometry and Qwen3 32B has 26.

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