gpt-oss-120b vs Qwen3 VL 235B A22B Instruct
gpt-oss-120b leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 11.6 to 9.9, and gpt-oss-120b costs 3.8x less per output token ($0.50 vs $1.90 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
gpt-oss-120b or Qwen3 VL 235B A22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick gpt-oss-120b for output tokens 3.8x cheaper ($0.50 vs $1.90 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 39
- gpt-oss-120b only
- 46
- Qwen3 VL 235B A22B Instruct only
- 10
- Like-for-like categories
- 7 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 9.9.
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
No call
Only gpt-oss-120b has a Design Arena: websites result, 968.
Directional only
Long documents
Listed context window
Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0003, Qwen3 VL 235B A22B Instruct $0.0012 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 VL 235B A22B Instruct not benchmarked.
- Widest category
- Coding, 13.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): gpt-oss-120b 87.8% against Qwen3 VL 235B A22B Instruct 64.6%, 23.2 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 3.8x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Qwen3 VL 235B A22B Instruct 262K.
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 VL 235B A22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 3 | ||
| Math | 2 | 2 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 18 | ||
| All shared boards | 9 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | 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% | 64.6% | gpt-oss-120b |
| SciCode | 34.0% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | 11.4% | gpt-oss-120b |
| Terminal-Bench | 18.7% | Not measured | Only gpt-oss-120b measured |
| WeirdML | 48.2% | Not measured | Only gpt-oss-120b measured |
| ALE-Bench | 575.6 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: coding | 1379 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: software and IT | 1387 | 1442 | Qwen3 VL 235B A22B Instruct |
| 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 index10
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 9.9 | gpt-oss-120b |
| Epoch Capabilities Index | 139.8 | Not measured | Only gpt-oss-120b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | Not measured | Only gpt-oss-120b measured |
| τ²-Bench Telecom | 65.8% | 54.1% | 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 index23
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 77.2% | gpt-oss-120b |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | 11.9% | gpt-oss-120b |
| GPQA Diamond (Epoch AI run) | 75.8% | Not measured | Only gpt-oss-120b measured |
| 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 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: expert | 1356 | 1442 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts in English | 1368 | 1439 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: diagrams | Not measured | 1255 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: homework | Not measured | 1277 | Only Qwen3 VL 235B A22B Instruct measured |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | 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 | 88.3 | gpt-oss-120b |
| AIME 2025 (Artificial Analysis run) | 93.4% | 88.3% | gpt-oss-120b |
| OTIS Mock AIME | 88.9% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: math | 1389 | 1425 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: mathematical work | 1386 | 1419 | Qwen3 VL 235B A22B Instruct |
| MathArena | 15.1% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | 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% | 83.6% | Qwen3 VL 235B A22B Instruct |
| LMArena Text: science | 1382 | 1444 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: medicine and healthcare | 1388 | 1431 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: legal and government | 1355 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: business and finance | 1359 | 1433 | Qwen3 VL 235B A22B Instruct |
| 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 designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | Not measured | Only gpt-oss-120b measured |
| Design Arena: UI components | 924 | Not measured | Only gpt-oss-120b measured |
| Design Arena: game dev | 991 | Not measured | Only gpt-oss-120b measured |
| Design Arena: data viz | 987 | Not measured | Only gpt-oss-120b measured |
| Design Arena: 3D | 901 | Not measured | Only gpt-oss-120b measured |
| Design Arena: all code categories | 962 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| IFBench | 69.0% | 56.5% | gpt-oss-120b |
| Long context reasoning (AA-LCR) | 52.0% | 63.7% | Qwen3 VL 235B A22B Instruct |
| DTBench | 76.3% | Not measured | Only gpt-oss-120b measured |
| LMCA | 22.1% | Not measured | Only gpt-oss-120b measured |
| Fiction.LiveBench | 44.4% | Not measured | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | 77.1% | Not measured | Only gpt-oss-120b measured |
| Surface Evolver Bench | 25.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text | 1366 | 1420 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: instruction following | 1318 | 1406 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: creative writing | 1274 | 1366 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision | Not measured | 1247 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: English | 1375 | 1429 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: non-English | 1351 | 1405 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Chinese | 1385 | 1463 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: German | 1353 | 1424 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Japanese | 1331 | 1385 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Korean | 1282 | 1394 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: French | 1370 | 1452 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Spanish | 1389 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Russian | 1343 | 1408 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Polish | 1353 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: multi-turn | 1340 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: longer queries | 1319 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: writing and language | 1306 | 1392 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: entertainment and media | 1292 | 1368 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: English | Not measured | 1256 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1283 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: reading text | Not measured | 1253 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: humor | Not measured | 1209 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: captioning | Not measured | 1281 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: recognizing things | Not measured | 1249 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3 VL 235B A22B Instruct measured |
| MultiChallenge | 45.3% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Qwen3 VL 235B A22B Instruct | 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 VL 235B A22B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | September 23, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.10 | $0.21 |
| Output $/Mtok | $0.50 | $1.90 |
| 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 VL 235B A22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 3.8x cheaper ($0.50 vs $1.90 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, gpt-oss-120b or Qwen3 VL 235B A22B Instruct?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct, with 262K tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Qwen3 VL 235B A22B Instruct comparison based on?
39 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 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.