gpt-oss-20b vs Qwen3.6 35B A3B
Qwen3.6 35B A3B leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 18.2 to 10.0, and gpt-oss-20b costs 6.7x less per output token ($0.15 vs $1.00 per million). Updated October 22, 2026; 15 benchmarks have a result for both.
gpt-oss-20b or Qwen3.6 35B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick gpt-oss-20b for output tokens 6.7x cheaper ($0.15 vs $1.00 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or Terminal-Bench, where it leads.
- Shared results
- 15
- gpt-oss-20b only
- 48
- Qwen3.6 35B A3B only
- 5
- Like-for-like categories
- 6 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
Qwen3.6 35B A3B
Qwen3.6 35B A3B leads, 18.2 against 10.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 35B A3B has a AA Agentic Index result, 13.1.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
No call
Only gpt-oss-20b has a Design Arena: websites result, 852.
Directional only
Long documents
Listed context window
Qwen3.6 35B A3B
Qwen3.6 35B A3B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
gpt-oss-20b $0.0001, Qwen3.6 35B A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- gpt-oss-20b not benchmarked, Qwen3.6 35B A3B not benchmarked.
- Widest category
- Agentic work, 35.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): gpt-oss-20b 34.7% against Qwen3.6 35B A3B 71.7%, 37.0 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-20b $0.15 against Qwen3.6 35B A3B $1.00 per million output tokens, 6.7x apart.
- Context window
- gpt-oss-20b takes 131K tokens, Qwen3.6 35B A3B 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-20b | Bars | Qwen3.6 35B A3B | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 1 | 3 | ||
| All shared boards | 3 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 41.9 | Only Qwen3.6 35B A3B measured |
| LiveCodeBench (Vals AI) | 80.4% | Not measured | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | 77.7% | Not measured | Only gpt-oss-20b measured |
| SciCode | 38.9% | 36.6% | gpt-oss-20b |
| Terminal-Bench Hard (Artificial Analysis run) | 10.6% | 34.8% | Qwen3.6 35B A3B |
| Terminal-Bench | 3.4% | 24.6% | Qwen3.6 35B A3B |
| WeirdML | 40.9% | 34.5% | gpt-oss-20b |
| ALE-Bench | 566.0 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: coding | 1307 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: software and IT | 1311 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.0 | 18.2 | Qwen3.6 35B A3B |
| Epoch Capabilities Index | 137.7 | 143.7 | Qwen3.6 35B A3B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 13.1 | Only Qwen3.6 35B A3B measured |
| τ²-Bench Telecom | 60.2% | 95.3% | Qwen3.6 35B A3B |
| BrowseComp-Plus | 50.6% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| GPQA Diamond | 68.9% | Not measured | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 68.8% | 84.1% | Qwen3.6 35B A3B |
| Humanity's Last Exam (Artificial Analysis run) | 11.0% | 22.2% | Qwen3.6 35B A3B |
| GPQA Diamond (Epoch AI run) | 60.8% | 84.8% | Qwen3.6 35B A3B |
| Mystery game puzzles | Not measured | 22.0% | Only Qwen3.6 35B A3B measured |
| LMArena Text: hard prompts | 1274 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: expert | 1258 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts in English | 1292 | Not measured | Only gpt-oss-20b measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AIME | 86.0% | Not measured | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | 94.2% | Not measured | Only gpt-oss-20b measured |
| MGSM (Vals AI) | 89.0% | Not measured | Only gpt-oss-20b measured |
| AA Math Index | 89.3 | Not measured | Only gpt-oss-20b measured |
| AIME 2025 (Artificial Analysis run) | 89.3% | Not measured | Only gpt-oss-20b measured |
| OTIS Mock AIME | 65.3% | 86.7% | Qwen3.6 35B A3B |
| FrontierMath, tiers 1 to 3 | Not measured | 20.4% | Only Qwen3.6 35B A3B measured |
| LMArena Text: math | 1317 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: mathematical work | 1305 | Not measured | Only gpt-oss-20b measured |
| MathArena | 12.8% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| MMLU Pro | 71.6% | Not measured | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | 70.8% | Not measured | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | 43.8% | Not measured | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | 53.1% | Not measured | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | 63.7% | Not measured | Only gpt-oss-20b measured |
| MedQA (Vals AI) | 82.9% | Not measured | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: science | 1296 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: medicine and healthcare | 1310 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: legal and government | 1280 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: business and finance | 1284 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 852 | Not measured | Only gpt-oss-20b measured |
| Design Arena: data viz | 933 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | gpt-oss-20b | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| IFBench | 65.1% | 64.4% | gpt-oss-20b |
| Long context reasoning (AA-LCR) | 34.7% | 71.7% | Qwen3.6 35B A3B |
| DTBench | 68.0% | 73.9% | Qwen3.6 35B A3B |
| LMCA | 14.5% | 29.7% | Qwen3.6 35B A3B |
| Surface Evolver Bench | Not measured | 44.4% | Only Qwen3.6 35B A3B measured |
| LMArena Text | 1288 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: instruction following | 1236 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: creative writing | 1201 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: English | 1303 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: non-English | 1269 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Chinese | 1315 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: German | 1255 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Japanese | 1244 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Korean | 1236 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Spanish | 1267 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Russian | 1278 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Polish | 1252 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | 1268 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: longer queries | 1250 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: writing and language | 1238 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: entertainment and media | 1216 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-20b | Qwen3.6 35B 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-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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-20b | Qwen3.6 35B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | April 27, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.03 | $0.10 |
| Output $/Mtok | $0.15 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-20b better than Qwen3.6 35B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-20b for output tokens 6.7x cheaper ($0.15 vs $1.00 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or Terminal-Bench, where it leads.
Which is cheaper, gpt-oss-20b or Qwen3.6 35B A3B?
gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, gpt-oss-20b or Qwen3.6 35B A3B?
Qwen3.6 35B A3B, with 262K tokens against 131K.
How many benchmarks is the gpt-oss-20b vs Qwen3.6 35B A3B comparison based on?
15 benchmarks have a result for both models as of October 22, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks and Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.