GPT-5.2-Codex vs Qwen3 30B A3B
GPT-5.2-Codex leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 28.5 to 7.6, and Qwen3 30B A3B costs 28.0x less per output token ($0.50 vs $14.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
GPT-5.2-Codex or Qwen3 30B A3B: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding.
Pick Qwen3 30B A3B for output tokens 28.0x cheaper ($0.50 vs $14.00 per million).
- Shared results
- 7
- GPT-5.2-Codex only
- 15
- Qwen3 30B A3B only
- 45
- 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 7.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3 30B A3B has a Design Arena: websites result, 955.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B
GPT-5.2-Codex $0.0088, Qwen3 30B A3B $0.0004 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 30B A3B not benchmarked.
- Widest category
- Agentic work, 66.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.2-Codex 82.3% against Qwen3 30B A3B 0.0%, 82.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Qwen3 30B A3B $0.50 per million output tokens, 28.0x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Qwen3 30B A3B 41K.
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 30B A3B | 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 30B A3B | Leader |
|---|---|---|---|
| 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 | 50.6% | Only Qwen3 30B A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 6.8% | GPT-5.2-Codex |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| WeirdML | Not measured | 29.8% | Only Qwen3 30B A3B measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1337 | Only Qwen3 30B A3B measured |
| LMArena Text: software and IT | Not measured | 1340 | Only Qwen3 30B A3B 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 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 7.6 | GPT-5.2-Codex |
| Epoch Capabilities Index | Not measured | 137.3 | Only Qwen3 30B A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | 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% | 26.0% | GPT-5.2-Codex |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 61.6% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 6.2% | GPT-5.2-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 61.7% | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: expert | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts in English | Not measured | 1329 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 72.3 | Only Qwen3 30B A3B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 72.3% | Only Qwen3 30B A3B measured |
| OTIS Mock AIME | Not measured | 62.8% | Only Qwen3 30B A3B measured |
| LMArena Text: math | Not measured | 1355 | Only Qwen3 30B A3B measured |
| LMArena Text: mathematical work | Not measured | 1363 | Only Qwen3 30B A3B 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 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.7% | Only Qwen3 30B A3B measured |
| LMArena Text: science | Not measured | 1321 | Only Qwen3 30B A3B measured |
| LMArena Text: medicine and healthcare | Not measured | 1318 | Only Qwen3 30B A3B measured |
| LMArena Text: legal and government | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: business and finance | Not measured | 1309 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 955 | Only Qwen3 30B A3B measured |
| Design Arena: UI components | Not measured | 946 | Only Qwen3 30B A3B measured |
| Design Arena: game dev | Not measured | 899 | Only Qwen3 30B A3B measured |
| Design Arena: data viz | Not measured | 967 | Only Qwen3 30B A3B measured |
| Design Arena: all code categories | Not measured | 942 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | Leader |
|---|---|---|---|
| IFBench | 77.6% | 41.5% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 82.3% | 0.0% | GPT-5.2-Codex |
| DTBench | Not measured | 60.3% | Only Qwen3 30B A3B measured |
| LMCA | Not measured | 15.8% | Only Qwen3 30B A3B measured |
| Fiction.LiveBench | Not measured | 40.6% | Only Qwen3 30B A3B measured |
| Creative writing (Lech Mazur) | Not measured | 75.3% | Only Qwen3 30B A3B measured |
| LMArena Text | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: instruction following | Not measured | 1283 | Only Qwen3 30B A3B measured |
| LMArena Text: creative writing | Not measured | 1271 | Only Qwen3 30B A3B measured |
| LMArena Text: English | Not measured | 1335 | Only Qwen3 30B A3B measured |
| LMArena Text: non-English | Not measured | 1295 | Only Qwen3 30B A3B measured |
| LMArena Text: Chinese | Not measured | 1347 | Only Qwen3 30B A3B measured |
| LMArena Text: German | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: Japanese | Not measured | 1254 | Only Qwen3 30B A3B measured |
| LMArena Text: Korean | Not measured | 1261 | Only Qwen3 30B A3B measured |
| LMArena Text: French | Not measured | 1352 | Only Qwen3 30B A3B measured |
| LMArena Text: Spanish | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: Russian | Not measured | 1291 | Only Qwen3 30B A3B measured |
| LMArena Text: Polish | Not measured | 1299 | Only Qwen3 30B A3B measured |
| LMArena Text: multi-turn | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: longer queries | Not measured | 1312 | Only Qwen3 30B A3B measured |
| LMArena Text: writing and language | Not measured | 1290 | Only Qwen3 30B A3B measured |
| LMArena Text: entertainment and media | Not measured | 1258 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3 30B A3B | 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 30B A3B costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B 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 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | April 28, 2025 |
| Context window | 400K | 41K |
| Input $/Mtok | $1.75 | $0.12 |
| Output $/Mtok | $14.00 | $0.50 |
| 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 30B A3B for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B for output tokens 28.0x cheaper ($0.50 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Qwen3 30B A3B?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Qwen3 30B A3B?
GPT-5.2-Codex, with 400K tokens against 41K.
How many benchmarks is the GPT-5.2-Codex vs Qwen3 30B A3B 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 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.