Noometry

Model comparison

Codestral vs Qwen3-30B-A3B

Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 30.1 on the Noometry Index.

Last verified . 1 shared benchmarks.

Codestral Mistral AI

30.1

Rank #287 Reported

Qwen3-30B-A3B Alibaba (Qwen)

38.5

Rank #174 Confirmed

Summary

  • They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and Qwen3-30B-A3B in 2 categories; 2 gaps are clear of the uncertainty.
  • The widest gap is in coding, where Qwen3-30B-A3B leads 37.0 to 26.9.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 32.5% for Codestral and 54.9% for Qwen3-30B-A3B.
  • Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.30 / $0.90 for Codestral.
  • Codestral accepts more context: 256K tokens versus 41K.

Side by side

CodestralQwen3-30B-A3B
ProviderMistral AIAlibaba (Qwen)
Noometry Index30.138.5
Released2024-05-292025-04-28
WeightsOpenOpen
Context window256K41K
Max output4K16K
Input $ / M tokens$0.30$0.12
Output $ / M tokens$0.90$0.50
Results tracked732

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Category by category

Coding Qwen3-30B-A3B leads

Codestral: 26.9 (#316), Qwen3-30B-A3B: 37.0 (#188)

BenchmarkCodestralQwen3-30B-A3B
Aider Polyglot11.1%—
SciCode—33.3%
WeirdML—29.8%
BigCodeBench Instruct41.8%—
LMArena Coding—1416
BigCodeBench Complete52.5%—
ALE-Bench137.78—
HumanEval+73.8%—
MBPP+61.9%—

Agentic & Tool Use Not comparable

Codestral: —, Qwen3-30B-A3B: 29.5 (#75)

BenchmarkCodestralQwen3-30B-A3B
Berkeley Function Calling Leaderboard—41.4%

Reasoning Qwen3-30B-A3B leads

Codestral: 19.1 (#244), Qwen3-30B-A3B: 21.6 (#193)

BenchmarkCodestralQwen3-30B-A3B
Kagi LLM Benchmark32.5%54.9%
CritPt—0.3%
Chess Puzzles—8%
LMArena Hard Prompts—1398
DTBench—69.3%
LMCA—22.4%
Epoch Capabilities Index—139.63

Math Not comparable

Codestral: —, Qwen3-30B-A3B: 36.0 (#133)

BenchmarkCodestralQwen3-30B-A3B
MathArena Final-Answer Competitions—47.8%
OTIS Mock AIME 2024-2025—70.3%
LMArena Math—1394

Knowledge Not comparable

Codestral: —, Qwen3-30B-A3B: 41.7 (#96)

BenchmarkCodestralQwen3-30B-A3B
GPQA Diamond—70.1%
Confabulations—12.3%
LMArena Expert—1395

Multilingual Not comparable

Codestral: —, Qwen3-30B-A3B: 49.8 (#126)

BenchmarkCodestralQwen3-30B-A3B
LMArena Non-English—1372
LMArena Chinese—1434
LMArena French—1419
LMArena German—1379
LMArena Japanese—1338
LMArena Korean—1333
LMArena Russian—1369
LMArena Spanish—1403

Instruction Following Not comparable

Codestral: —, Qwen3-30B-A3B: 72.2 (#137)

BenchmarkCodestralQwen3-30B-A3B
LMArena Instruction Following—1364

Long Context Not comparable

Codestral: —, Qwen3-30B-A3B: 31.0 (#278)

BenchmarkCodestralQwen3-30B-A3B
Fiction.LiveBench—40.6%
LMArena Longer Query—1379

Writing & Preference Not comparable

Codestral: —, Qwen3-30B-A3B: 55.9 (#138)

BenchmarkCodestralQwen3-30B-A3B
LMArena Text—1384
LMArena Creative Writing—1318
Short-Story Creative Writing—75.3%
LMArena Multi-Turn—1378

Frequently asked questions

Is Codestral better than Qwen3-30B-A3B?

Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 30.1 on the Noometry Index.

Which is cheaper, Codestral or Qwen3-30B-A3B?

Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; Codestral lists at $0.30 and $0.90.

Is Codestral or Qwen3-30B-A3B better for coding?

Qwen3-30B-A3B scores higher on coding benchmarks: 37.0 versus 26.9 in the Noometry coding category.

Which has the bigger context window?

Codestral does, with 256K tokens against 41K.

How many benchmarks do Codestral and Qwen3-30B-A3B share?

1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and Qwen3-30B-A3B has 32.

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