GPT-5.2-Codex vs Qwen3 32B
GPT-5.2-Codex leads Qwen3 32B on the Artificial Analysis Intelligence Index, 28.5 to 7.3, and Qwen3 32B costs 35.0x less per output token ($0.40 vs $14.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
GPT-5.2-Codex or Qwen3 32B: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding.
Pick Qwen3 32B for output tokens 35.0x cheaper ($0.40 vs $14.00 per million).
- Shared results
- 7
- GPT-5.2-Codex only
- 15
- Qwen3 32B only
- 38
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 7.3.
Same board, both measured
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
GPT-5.2-Codex $0.0088, Qwen3 32B $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Qwen3 32B not benchmarked.
- Widest category
- Agentic work, 62.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.2-Codex 82.3% against Qwen3 32B 0.0%, 82.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Qwen3 32B $0.40 per million output tokens, 35.0x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Qwen3 32B 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.2-Codex | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 54.6% | Only Qwen3 32B measured |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 3.0% | GPT-5.2-Codex |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1357 | Only Qwen3 32B measured |
| LMArena Text: software and IT | Not measured | 1366 | Only Qwen3 32B measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 7.3 | GPT-5.2-Codex |
| Epoch Capabilities Index | Not measured | 138.4 | Only Qwen3 32B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | 29.8% | GPT-5.2-Codex |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 48.7% | Only Qwen3 32B measured |
| BrowseComp-Plus | Not measured | 10.7% | Only Qwen3 32B measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 66.8% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 7.4% | GPT-5.2-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| LMArena Text: hard prompts | Not measured | 1334 | Only Qwen3 32B measured |
| LMArena Text: expert | Not measured | 1362 | Only Qwen3 32B measured |
| LMArena Text: hard prompts in English | Not measured | 1350 | Only Qwen3 32B measured |
| Kagi LLM Benchmark | Not measured | 48.7% | Only Qwen3 32B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 73.0 | Only Qwen3 32B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only Qwen3 32B measured |
| OTIS Mock AIME | Not measured | 66.9% | Only Qwen3 32B measured |
| LMArena Text: math | Not measured | 1399 | Only Qwen3 32B measured |
| LMArena Text: mathematical work | Not measured | 1420 | Only Qwen3 32B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 79.8% | Only Qwen3 32B measured |
| LMArena Text: science | Not measured | 1372 | Only Qwen3 32B measured |
| LMArena Text: medicine and healthcare | Not measured | 1368 | Only Qwen3 32B measured |
| LMArena Text: legal and government | Not measured | 1324 | Only Qwen3 32B measured |
| LMArena Text: business and finance | Not measured | 1318 | Only Qwen3 32B measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| IFBench | 77.6% | 36.3% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 82.3% | 0.0% | GPT-5.2-Codex |
| DTBench | Not measured | 67.5% | Only Qwen3 32B measured |
| LMCA | Not measured | 17.3% | Only Qwen3 32B measured |
| Fiction.LiveBench | Not measured | 74.2% | Only Qwen3 32B measured |
| LMArena Text | Not measured | 1340 | Only Qwen3 32B measured |
| LMArena Text: instruction following | Not measured | 1305 | Only Qwen3 32B measured |
| LMArena Text: creative writing | Not measured | 1297 | Only Qwen3 32B measured |
| LMArena Text: English | Not measured | 1359 | Only Qwen3 32B measured |
| LMArena Text: non-English | Not measured | 1317 | Only Qwen3 32B measured |
| LMArena Text: Chinese | Not measured | 1357 | Only Qwen3 32B measured |
| LMArena Text: German | Not measured | 1341 | Only Qwen3 32B measured |
| LMArena Text: Russian | Not measured | 1311 | Only Qwen3 32B measured |
| LMArena Text: multi-turn | Not measured | 1331 | Only Qwen3 32B measured |
| LMArena Text: longer queries | Not measured | 1327 | Only Qwen3 32B measured |
| LMArena Text: writing and language | Not measured | 1298 | Only Qwen3 32B measured |
| LMArena Text: entertainment and media | Not measured | 1279 | Only Qwen3 32B measured |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 32B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.2-Codex | Qwen3 32B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | April 28, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.75 | $0.14 |
| Output $/Mtok | $14.00 | $0.40 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Qwen3 32B for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding. Pick Qwen3 32B for output tokens 35.0x cheaper ($0.40 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Qwen3 32B?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Qwen3 32B?
GPT-5.2-Codex, with 400K tokens against 131K.
How many benchmarks is the GPT-5.2-Codex vs Qwen3 32B comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Qwen3 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.