Noometry

Model comparison

Mixtral 8x7B vs Qwen3 14B

Qwen3 14B is the stronger model overall, scoring 35.5 to 27.1 on the Noometry Index.

Last verified . 3 shared benchmarks.

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Qwen3 14B Alibaba (Qwen)

35.5

Rank #225 Confirmed

Summary

  • They share 3 benchmarks with published results for both. Mixtral 8x7B scores higher in 0 categories and Qwen3 14B in 5 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Qwen3 14B leads 39.3 to 11.0.
  • The biggest single-benchmark swing is GPQA Diamond: 30.6% for Mixtral 8x7B and 63.8% for Qwen3 14B.
  • Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
  • Qwen3 14B accepts more context: 131K tokens versus 32K.

Side by side

Mixtral 8x7BQwen3 14B
ProviderMistral AIAlibaba (Qwen)
Noometry Index27.135.5
Released2023-12-112025-04
WeightsOpenOpen
Context window32K131K
Max output32K8K
Input $ / M tokens$0.70$0.35
Output $ / M tokens$0.70$1.40
Results tracked3812

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Category by category

Coding Qwen3 14B leads

Mixtral 8x7B: 32.8 (#269), Qwen3 14B: 37.3 (#195)

BenchmarkMixtral 8x7BQwen3 14B
SciCode—31.6%
LMArena Coding1126—
HumanEval+39.6%—
MBPP+49.7%—

Agentic & Tool Use Not comparable

Mixtral 8x7B: —, Qwen3 14B: 29.6 (#83)

BenchmarkMixtral 8x7BQwen3 14B
Berkeley Function Calling Leaderboard—41%

Reasoning Too close to call

Mixtral 8x7B: 18.2 (#285), Qwen3 14B: 18.5 (#280)

BenchmarkMixtral 8x7BQwen3 14B
DTBench49.6%64%
Epoch Capabilities Index118.47138.23
Kagi LLM Benchmark—49.1%
CritPt—0%
Chess Puzzles—4%
LMArena Hard Prompts1115—
LMCA—18.2%
Adversarial NLI55.2%—
ForecastBench56.3—
HellaSwag86.7%—
PIQA83.6%—
WinoGrande77.2%—

Math Qwen3 14B leads

Mixtral 8x7B: 18.8 (#289), Qwen3 14B: 38.6 (#133)

BenchmarkMixtral 8x7BQwen3 14B
OTIS Mock AIME 2024-2025—66.4%
Omni-MATH10.5%—
LMArena Math1147—
MATH Level 510%—
GSM8K74.4%—

Knowledge Qwen3 14B leads

Mixtral 8x7B: 11.0 (#301), Qwen3 14B: 39.3 (#134)

BenchmarkMixtral 8x7BQwen3 14B
GPQA Diamond30.6%63.8%
MMLU-Pro33.5%—
Vectara Hallucination Rate—5.4%
GPQA (HELM)29.6%—
LMArena Expert1088—
ARC (AI2) Challenge87.3%—
MMLU70.6%—
OpenBookQA85.8%—
TriviaQA82.2%—

Multilingual Not comparable

Mixtral 8x7B: 29.6 (#266), Qwen3 14B: —

BenchmarkMixtral 8x7BQwen3 14B
LMArena Non-English1077—
LMArena Chinese1055—
LMArena French1166—
LMArena German1114—
LMArena Japanese931—
LMArena Korean968—
LMArena Russian1090—
LMArena Spanish1111—

Instruction Following Not comparable

Mixtral 8x7B: 51.0 (#297), Qwen3 14B: —

BenchmarkMixtral 8x7BQwen3 14B
IFEval57.5%—
LMArena Instruction Following1109—

Long Context Qwen3 14B leads

Mixtral 8x7B: 33.4 (#260), Qwen3 14B: 38.1 (#204)

BenchmarkMixtral 8x7BQwen3 14B
Fiction.LiveBench—62.5%
LMArena Longer Query1103—

Writing & Preference Not comparable

Mixtral 8x7B: 34.2 (#270), Qwen3 14B: —

BenchmarkMixtral 8x7BQwen3 14B
LMArena Text1132—
LMArena Creative Writing1109—
WildBench67.3%—
LMArena Multi-Turn1115—

Frequently asked questions

Is Mixtral 8x7B better than Qwen3 14B?

Qwen3 14B is the stronger model overall, scoring 35.5 to 27.1 on the Noometry Index.

Which is cheaper, Mixtral 8x7B or Qwen3 14B?

Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

Is Mixtral 8x7B or Qwen3 14B better for coding?

Qwen3 14B scores higher on coding benchmarks: 37.3 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

Qwen3 14B does, with 131K tokens against 32K.

How many benchmarks do Mixtral 8x7B and Qwen3 14B share?

3 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and Qwen3 14B has 12.

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