Model comparison
GPT-5.2 Codex vs Qwen3 32B
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 39.2 on the Noometry Index. Qwen3 32B costs 3.9× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 41.0 to 32.6.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 Codex | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.6 | 39.2 |
| Released | 2025-12-18 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $1.75 | $0.70 |
| Output $ / M tokens | $14 | $2.80 |
| Results tracked | 5 | 26 |
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Category by category
Coding GPT-5.2 Codex leads
GPT-5.2 Codex: 45.5 (#71), Qwen3 32B: 37.7 (#190)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | — |
| Aider Polyglot | — | 40% |
| LMArena WebDev | 1339 | — |
| SWE-bench Multilingual | 66.3% | — |
| SciCode | — | 35.4% |
| LMArena Coding | — | 1358 |
| ALE-Bench | 1,300 | — |
Agentic & Tool Use GPT-5.2 Codex leads
GPT-5.2 Codex: 41.0 (#22), Qwen3 32B: 32.6 (#62)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| Terminal-Bench | 66.5% | — |
| Berkeley Function Calling Leaderboard | — | 48.7% |
Reasoning Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 20.2 (#241)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 5% |
| LMArena Hard Prompts | — | 1334 |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
| Epoch Capabilities Index | — | 138.51 |
Math Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 39.7 (#99)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.9% |
| LMArena Math | — | 1399 |
Knowledge Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 40.0 (#125)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| GPQA Diamond | — | 65.7% |
| Vectara Hallucination Rate | — | 5.9% |
| LMArena Expert | — | 1362 |
Multilingual Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 45.6 (#167)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| LMArena Non-English | — | 1317 |
| LMArena Chinese | — | 1357 |
| LMArena German | — | 1341 |
| LMArena Russian | — | 1311 |
Instruction Following Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 68.9 (#179)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | — | 1305 |
Long Context Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 43.8 (#87)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | — | 74.2% |
| LMArena Longer Query | — | 1327 |
Writing & Preference Not comparable
GPT-5.2 Codex: —, Qwen3 32B: 52.9 (#163)
| Benchmark | GPT-5.2 Codex | Qwen3 32B |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1297 |
| LMArena Multi-Turn | — | 1331 |
Frequently asked questions
Is GPT-5.2 Codex better than Qwen3 32B?
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 39.2 on the Noometry Index. Qwen3 32B costs 3.9× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 Codex or Qwen3 32B?
Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.
Is GPT-5.2 Codex or Qwen3 32B better for coding?
GPT-5.2 Codex scores higher on coding benchmarks: 45.5 versus 37.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 Codex does, with 400K tokens against 131K.
How many benchmarks do GPT-5.2 Codex and Qwen3 32B share?
0 benchmarks have published results for both models. GPT-5.2 Codex has 5 scored results on Noometry and Qwen3 32B has 26.