gpt-oss-120b vs Qwen-Plus
gpt-oss-120b costs 1.6x less per output token than Qwen-Plus ($0.50 vs $0.78 per million). Updated October 21, 2026; 27 benchmarks have a result for both.
gpt-oss-120b or Qwen-Plus: 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 1.6x cheaper ($0.50 vs $0.78 per million) and work measured by DTBench or LMCA, where it leads.
- Shared results
- 27
- gpt-oss-120b only
- 54
- Qwen-Plus only
- 1
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only gpt-oss-120b has a AA Intelligence Index result, 11.6.
Directional only
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
Qwen-Plus
Qwen-Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0003, Qwen-Plus $0.0006 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, Qwen-Plus not benchmarked.
- Widest category
- Math, 30.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: gpt-oss-120b 88.9% against Qwen-Plus 17.8%, 71.1 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Qwen-Plus $0.78 per million output tokens, 1.6x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Qwen-Plus 1M.
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 | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 10 | 4 | ||
| All shared boards | 23 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | gpt-oss-120b | Qwen-Plus | 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% | Not measured | Only gpt-oss-120b measured |
| SciCode | 34.0% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only gpt-oss-120b measured |
| 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 | 1328 | gpt-oss-120b |
| LMArena Text: software and IT | 1387 | 1343 | gpt-oss-120b |
| SciCode | 7.7% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | gpt-oss-120b | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | Not measured | Only gpt-oss-120b measured |
| Epoch Capabilities Index | 139.8 | Not measured | Only gpt-oss-120b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Qwen-Plus | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | Not measured | Only gpt-oss-120b measured |
| τ²-Bench Telecom | 65.8% | Not measured | Only gpt-oss-120b measured |
| 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 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | gpt-oss-120b | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only gpt-oss-120b measured |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | Not measured | Only gpt-oss-120b measured |
| GPQA Diamond (Epoch AI run) | 75.8% | 48.1% | 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 | 1317 | gpt-oss-120b |
| LMArena Text: expert | 1356 | 1328 | gpt-oss-120b |
| LMArena Text: hard prompts in English | 1368 | 1333 | gpt-oss-120b |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | gpt-oss-120b | Qwen-Plus | 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 | Not measured | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | 93.4% | Not measured | Only gpt-oss-120b measured |
| OTIS Mock AIME | 88.9% | 17.8% | gpt-oss-120b |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| LMArena Text: math | 1389 | 1326 | gpt-oss-120b |
| LMArena Text: mathematical work | 1386 | 1332 | gpt-oss-120b |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | gpt-oss-120b | Qwen-Plus | 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% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: science | 1382 | 1352 | gpt-oss-120b |
| LMArena Text: medicine and healthcare | 1388 | 1350 | gpt-oss-120b |
| LMArena Text: legal and government | 1355 | 1349 | gpt-oss-120b |
| LMArena Text: business and finance | 1359 | 1303 | gpt-oss-120b |
| 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 | Qwen-Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | Not measured | Only gpt-oss-120b measured |
| Design Arena: UI components | 927 | Not measured | Only gpt-oss-120b measured |
| Design Arena: game dev | 992 | 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 | 963 | 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 index104
| Benchmark | gpt-oss-120b | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 69.0% | Not measured | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 52.0% | Not measured | Only gpt-oss-120b measured |
| DTBench | 76.3% | 81.1% | Qwen-Plus |
| LMCA | 22.1% | 24.0% | Qwen-Plus |
| 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 | 1326 | gpt-oss-120b |
| LMArena Text: instruction following | 1318 | 1303 | gpt-oss-120b |
| LMArena Text: creative writing | 1274 | 1292 | Qwen-Plus |
| LMArena Text: English | 1375 | 1339 | gpt-oss-120b |
| LMArena Text: non-English | 1351 | 1310 | gpt-oss-120b |
| LMArena Text: Chinese | 1385 | 1347 | gpt-oss-120b |
| LMArena Text: German | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Japanese | 1331 | 1251 | gpt-oss-120b |
| LMArena Text: Korean | 1282 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: French | 1370 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Spanish | 1389 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Russian | 1343 | 1323 | gpt-oss-120b |
| LMArena Text: Polish | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | 1340 | 1336 | gpt-oss-120b |
| LMArena Text: longer queries | 1319 | 1324 | Qwen-Plus |
| LMArena Text: writing and language | 1306 | 1296 | gpt-oss-120b |
| LMArena Text: entertainment and media | 1292 | 1270 | gpt-oss-120b |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Qwen-Plus | 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 | Qwen-Plus |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | February 1, 2025 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.10 | $0.26 |
| Output $/Mtok | $0.50 | $0.78 |
| 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 Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 1.6x cheaper ($0.50 vs $0.78 per million) and work measured by DTBench or LMCA, where it leads.
Which is cheaper, gpt-oss-120b or Qwen-Plus?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Qwen-Plus?
Qwen-Plus, with 1M tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Qwen-Plus comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.