gpt-oss-120b vs Qwen3 30B A3B
gpt-oss-120b leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 11.6 to 7.6, at about the same output price ($0.50 vs $0.50 per million). Updated October 22, 2026; 53 benchmarks have a result for both.
gpt-oss-120b or Qwen3 30B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick gpt-oss-120b for a larger context window (131K against 41K tokens) and work measured by Design Arena: UI components, where it leads.
- Shared results
- 53
- gpt-oss-120b only
- 32
- Qwen3 30B A3B only
- 0
- Like-for-like categories
- 8 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
gpt-oss-120b
gpt-oss-120b leads, 11.6 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only gpt-oss-120b has a AA Agentic Index result, 3.7.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-120b has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-120b has a MMLU Pro result, 79.2%.
Directional only
Web design
Design Arena: websites
gpt-oss-120b
gpt-oss-120b leads, 968 against 954.
Same board, both measured
Long documents
Listed context window
gpt-oss-120b
gpt-oss-120b takes 131K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0003, Qwen3 30B A3B $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only gpt-oss-120b has the number, $0.51.
Directional only
At a glance
- Coding index
- gpt-oss-120b not benchmarked, Qwen3 30B A3B not benchmarked.
- Widest category
- Agentic work, 39.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): gpt-oss-120b 52.0% against Qwen3 30B A3B 0.0%, 52.0 points apart on a 0 to 100 scale.
- Output price
- Both cost $0.50 per million output tokens.
- Context window
- gpt-oss-120b takes 131K 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-oss-120b | Bars | Qwen3 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 5 | 0 | ||
| Web design | 4 | 1 | ||
| General | 23 | 0 | ||
| All shared boards | 52 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Aider polyglot | 41.8% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified | 26.0% | Not measured | Only gpt-oss-120b measured |
| AA Coding Index | 30.4 | Not measured | Only gpt-oss-120b measured |
| LiveCodeBench (Vals AI) | 83.2% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | 33.6% | Not measured | Only gpt-oss-120b measured |
| LiveCodeBench (Artificial Analysis run) | 87.8% | 50.6% | gpt-oss-120b |
| SciCode | 34.0% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | 6.8% | gpt-oss-120b |
| Terminal-Bench | 18.7% | Not measured | Only gpt-oss-120b measured |
| WeirdML | 48.2% | 29.8% | gpt-oss-120b |
| ALE-Bench | 575.6 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: coding | 1379 | 1337 | gpt-oss-120b |
| LMArena Text: software and IT | 1387 | 1340 | gpt-oss-120b |
| SciCode | 7.7% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 7.6 | gpt-oss-120b |
| Epoch Capabilities Index | 139.8 | 137.3 | gpt-oss-120b |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | Not measured | Only gpt-oss-120b measured |
| τ²-Bench Telecom | 65.8% | 26.0% | gpt-oss-120b |
| APEX-Agents | 4.4% | Not measured | Only gpt-oss-120b measured |
| METR time horizons | 56.6% | Not measured | Only gpt-oss-120b measured |
| Vending-Bench 2 | -$22 | Not measured | Only gpt-oss-120b measured |
| BrowseComp-Plus | 67.0% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 61.6% | gpt-oss-120b |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | 6.2% | gpt-oss-120b |
| GPQA Diamond (Epoch AI run) | 75.8% | 61.7% | gpt-oss-120b |
| SimpleBench | 22.1% | Not measured | Only gpt-oss-120b measured |
| Mystery game puzzles | 2.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | 1364 | 1314 | gpt-oss-120b |
| LMArena Text: expert | 1356 | 1314 | gpt-oss-120b |
| LMArena Text: hard prompts in English | 1368 | 1329 | gpt-oss-120b |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AIME | 92.6% | Not measured | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | 94.8% | Not measured | Only gpt-oss-120b measured |
| MGSM (Vals AI) | 92.0% | Not measured | Only gpt-oss-120b measured |
| AA Math Index | 93.4 | 72.3 | gpt-oss-120b |
| AIME 2025 (Artificial Analysis run) | 93.4% | 72.3% | gpt-oss-120b |
| OTIS Mock AIME | 88.9% | 62.8% | gpt-oss-120b |
| LMArena Text: math | 1389 | 1355 | gpt-oss-120b |
| LMArena Text: mathematical work | 1386 | 1363 | gpt-oss-120b |
| MathArena | 15.1% | 9.4% | gpt-oss-120b |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro | 79.2% | Not measured | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | 75.9% | Not measured | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | 48.8% | Not measured | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | 58.2% | Not measured | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | 71.6% | Not measured | Only gpt-oss-120b measured |
| MedQA (Vals AI) | 91.4% | Not measured | Only gpt-oss-120b measured |
| MMLU Pro (Artificial Analysis run) | 80.8% | 77.7% | gpt-oss-120b |
| LMArena Text: science | 1382 | 1321 | gpt-oss-120b |
| LMArena Text: medicine and healthcare | 1388 | 1318 | gpt-oss-120b |
| LMArena Text: legal and government | 1355 | 1314 | gpt-oss-120b |
| LMArena Text: business and finance | 1359 | 1309 | gpt-oss-120b |
| PRBench Finance | 43.8% | Not measured | Only gpt-oss-120b measured |
| PRBench Legal | 40.2% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | 954 | gpt-oss-120b |
| Design Arena: UI components | 924 | 943 | Qwen3 30B A3B |
| Design Arena: game dev | 991 | 898 | gpt-oss-120b |
| Design Arena: data viz | 987 | 967 | gpt-oss-120b |
| Design Arena: 3D | 901 | Not measured | Only gpt-oss-120b measured |
| Design Arena: all code categories | 962 | 941 | gpt-oss-120b |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index230
| Benchmark | gpt-oss-120b | Qwen3 30B A3B | Leader |
|---|---|---|---|
| IFBench | 69.0% | 41.5% | gpt-oss-120b |
| Long context reasoning (AA-LCR) | 52.0% | 0.0% | gpt-oss-120b |
| DTBench | 76.3% | 60.3% | gpt-oss-120b |
| LMCA | 22.1% | 15.8% | gpt-oss-120b |
| Fiction.LiveBench | 44.4% | 40.6% | gpt-oss-120b |
| Creative writing (Lech Mazur) | 77.1% | 75.3% | gpt-oss-120b |
| Surface Evolver Bench | 25.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text | 1366 | 1317 | gpt-oss-120b |
| LMArena Text: instruction following | 1318 | 1283 | gpt-oss-120b |
| LMArena Text: creative writing | 1274 | 1271 | gpt-oss-120b |
| LMArena Text: English | 1375 | 1335 | gpt-oss-120b |
| LMArena Text: non-English | 1351 | 1295 | gpt-oss-120b |
| LMArena Text: Chinese | 1385 | 1347 | gpt-oss-120b |
| LMArena Text: German | 1353 | 1307 | gpt-oss-120b |
| LMArena Text: Japanese | 1331 | 1254 | gpt-oss-120b |
| LMArena Text: Korean | 1282 | 1261 | gpt-oss-120b |
| LMArena Text: French | 1370 | 1352 | gpt-oss-120b |
| LMArena Text: Spanish | 1389 | 1317 | gpt-oss-120b |
| LMArena Text: Russian | 1343 | 1291 | gpt-oss-120b |
| LMArena Text: Polish | 1353 | 1299 | gpt-oss-120b |
| LMArena Text: multi-turn | 1340 | 1307 | gpt-oss-120b |
| LMArena Text: longer queries | 1319 | 1312 | gpt-oss-120b |
| LMArena Text: writing and language | 1306 | 1290 | gpt-oss-120b |
| LMArena Text: entertainment and media | 1292 | 1258 | gpt-oss-120b |
| MultiChallenge | 45.3% | Not measured | Only gpt-oss-120b 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-oss-120b | 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
gpt-oss-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-120b 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-oss-120b | Qwen3 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | April 28, 2025 |
| Context window | 131K | 41K |
| Input $/Mtok | $0.10 | $0.12 |
| Output $/Mtok | $0.50 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.51 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Qwen3 30B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for a larger context window (131K against 41K tokens) and work measured by Design Arena: UI components, where it leads.
Which is cheaper, gpt-oss-120b or Qwen3 30B A3B?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Qwen3 30B A3B?
gpt-oss-120b, with 131K tokens against 41K.
How many benchmarks is the gpt-oss-120b vs Qwen3 30B A3B comparison based on?
53 benchmarks have a result for both models as of October 22, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Qwen3 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.