Noometry

Model comparison

GPT-5-Codex vs Qwen3.5 122B-A10B

Qwen3.5 122B-A10B is the stronger model overall, scoring 41.6 to 37.3 on the Noometry Index.

Last verified . 0 shared benchmarks.

GPT-5-Codex OpenAI

37.3

Rank #190 Reported

Qwen3.5 122B-A10B Alibaba (Qwen)

41.6

Rank #120 Confirmed

Summary

  • The widest gap is in reasoning, where GPT-5-Codex leads 30.0 to 26.4.
  • Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
  • GPT-5-Codex accepts more context: 400K tokens versus 262K.
  • Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.

Side by side

GPT-5-CodexQwen3.5 122B-A10B
ProviderOpenAIAlibaba (Qwen)
Noometry Index37.341.6
Released2025-09-152026-02-23
WeightsProprietaryOpen
Context window400K262K
Max output128K66K
Input $ / M tokens$1.25$0.40
Output $ / M tokens$10$3.20
Results tracked327

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Category by category

Coding GPT-5-Codex leads

GPT-5-Codex: 41.9 (#100), Qwen3.5 122B-A10B: 38.8 (#155)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena WebDev—1360
SciCode—35.6%
WeirdML54.5%—
LMArena Coding—1436

Agentic & Tool Use Not comparable

GPT-5-Codex: 30.8 (#66), Qwen3.5 122B-A10B: —

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
Terminal-Bench44.3%—

Reasoning GPT-5-Codex leads

GPT-5-Codex: 30.0 (#80), Qwen3.5 122B-A10B: 26.4 (#121)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
Kagi LLM Benchmark70.3%—
NYT Connections (extended)—51.7%
CritPt—0.9%
Thematic Generalization—51.2%
LMArena Hard Prompts—1421
Mystery Game Puzzles—17%
DTBench—84.3%
LMCA—32.2%

Math Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 36.4 (#114)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Math—1432

Knowledge Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 38.2 (#138)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
Vectara Hallucination Rate—11.2%
LMArena Expert—1431

Multimodal Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 39.0 (#52)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Vision—1245

Multilingual Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 51.8 (#101)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Non-English—1400
LMArena Chinese—1462
LMArena French—1442
LMArena German—1425
LMArena Japanese—1364
LMArena Korean—1353
LMArena Russian—1400
LMArena Spanish—1423

Instruction Following Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 74.0 (#109)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Instruction Following—1399

Long Context Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 43.0 (#105)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Longer Query—1410

Writing & Preference Not comparable

GPT-5-Codex: —, Qwen3.5 122B-A10B: 60.2 (#98)

BenchmarkGPT-5-CodexQwen3.5 122B-A10B
LMArena Text—1417
LMArena Creative Writing—1369
LMArena Multi-Turn—1416

Frequently asked questions

Is GPT-5-Codex better than Qwen3.5 122B-A10B?

Qwen3.5 122B-A10B is the stronger model overall, scoring 41.6 to 37.3 on the Noometry Index.

Which is cheaper, GPT-5-Codex or Qwen3.5 122B-A10B?

Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; GPT-5-Codex lists at $1.25 and $10.

Is GPT-5-Codex or Qwen3.5 122B-A10B better for coding?

GPT-5-Codex scores higher on coding benchmarks: 41.9 versus 38.8 in the Noometry coding category.

Which has the bigger context window?

GPT-5-Codex does, with 400K tokens against 262K.

How many benchmarks do GPT-5-Codex and Qwen3.5 122B-A10B share?

0 benchmarks have published results for both models. GPT-5-Codex has 3 scored results on Noometry and Qwen3.5 122B-A10B has 27.

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