o3 vs Qwen3.6 35B A3B
o3 leads Qwen3.6 35B A3B on the Artificial Analysis Intelligence Index, 20.2 to 18.2, and Qwen3.6 35B A3B costs 8.0x less per output token ($1.00 vs $8.00 per million). Updated October 21, 2026; 15 benchmarks have a result for both.
o3 or Qwen3.6 35B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick o3 for work measured by τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 15
- o3 only
- 87
- 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
o3
o3 leads, 20.2 against 18.2.
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 o3 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
No call
Only o3 has a MMLU Pro result, 85.6%.
Directional only
Web design
Design Arena: websites
No call
Only o3 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Qwen3.6 35B A3B
Qwen3.6 35B A3B takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 35B A3B
o3 $0.0060, Qwen3.6 35B A3B $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- o3 not benchmarked, Qwen3.6 35B A3B not benchmarked.
- Widest category
- Agentic work, 14.6 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: o3 52.4% against Qwen3.6 35B A3B 34.5%, 17.9 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3.6 35B A3B $1.00 per million output tokens, 8.0x apart.
- Context window
- o3 takes 200K 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 | o3 | Bars | Qwen3.6 35B A3B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 3 | ||
| Math | 1 | 1 | ||
| General | 4 | 0 | ||
| All shared boards | 10 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| Aider polyglot | 81.3% | Not measured | Only o3 measured |
| SWE-bench Verified | 58.4% | Not measured | Only o3 measured |
| SWE-bench Multimodal | 36.0% | Not measured | Only o3 measured |
| AA Coding Index | Not measured | 41.9 | Only Qwen3.6 35B A3B measured |
| LiveCodeBench (Vals AI) | 83.9% | Not measured | Only o3 measured |
| LiveCodeBench (Artificial Analysis run) | 80.8% | Not measured | Only o3 measured |
| SciCode | Not measured | 36.6% | Only Qwen3.6 35B A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 34.8% | o3 |
| Terminal-Bench | Not measured | 24.6% | Only Qwen3.6 35B A3B measured |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | 34.5% | o3 |
| ALE-Bench | 933.5 | Not measured | Only o3 measured |
| LMArena Text: coding | 1407 | Not measured | Only o3 measured |
| LMArena Text: software and IT | 1415 | Not measured | Only o3 measured |
| SciCode | 9.2% | Not measured | Only o3 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | 18.2 | o3 |
| Epoch Capabilities Index | 146.7 | 143.7 | o3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 13.1 | Only Qwen3.6 35B A3B measured |
| τ²-Bench Telecom | 80.7% | 95.3% | Qwen3.6 35B A3B |
| METR time horizons | 65.4% | Not measured | Only o3 measured |
| GDPval | 30.8% | Not measured | Only o3 measured |
| DeepResearch Bench | 45.2% | Not measured | Only o3 measured |
| OSWorld | 23.0% | Not measured | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 63.0% | Not measured | Only o3 measured |
| BrowseComp-Plus | 65.9% | Not measured | Only o3 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| GPQA Diamond | 84.1% | Not measured | Only o3 measured |
| MMMU Pro (Vals AI) | 80.4% | Not measured | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 82.7% | 84.1% | Qwen3.6 35B A3B |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | 22.2% | Qwen3.6 35B A3B |
| GPQA Diamond (Epoch AI run) | 81.8% | 84.8% | Qwen3.6 35B A3B |
| ARC-AGI | 60.8% | Not measured | Only o3 measured |
| ARC-AGI-2 | 6.5% | Not measured | Only o3 measured |
| SimpleBench | 53.1% | Not measured | Only o3 measured |
| Chess puzzles | 37.0% | Not measured | Only o3 measured |
| Mystery game puzzles | 29.0% | 22.0% | o3 |
| VPCT | 52.0% | Not measured | Only o3 measured |
| LMArena Text: hard prompts | 1402 | Not measured | Only o3 measured |
| LMArena Text: expert | 1402 | Not measured | Only o3 measured |
| LMArena Text: hard prompts in English | 1411 | Not measured | Only o3 measured |
| LMArena Vision: diagrams | 1220 | Not measured | Only o3 measured |
| LMArena Vision: homework | 1234 | Not measured | Only o3 measured |
| Kagi LLM Benchmark | 67.6% | Not measured | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AIME | 85.3% | Not measured | Only o3 measured |
| MATH 500 (Vals AI) | 94.6% | Not measured | Only o3 measured |
| MGSM (Vals AI) | 91.8% | Not measured | Only o3 measured |
| AA Math Index | 88.3 | Not measured | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | 88.3% | Not measured | Only o3 measured |
| OTIS Mock AIME | 84.4% | 86.7% | Qwen3.6 35B A3B |
| FrontierMath, tiers 1 to 3 | 33.3% | 20.4% | o3 |
| MATH level 5 | 97.8% | Not measured | Only o3 measured |
| LMArena Text: math | 1426 | Not measured | Only o3 measured |
| LMArena Text: mathematical work | 1423 | Not measured | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| MMLU Pro | 85.6% | Not measured | Only o3 measured |
| LegalBench (Vals AI) | 83.8% | Not measured | Only o3 measured |
| CorpFin (Vals AI) | 59.7% | Not measured | Only o3 measured |
| TaxEval (Vals AI) | 74.6% | Not measured | Only o3 measured |
| MortgageTax (Vals AI) | 65.7% | Not measured | Only o3 measured |
| MedQA (Vals AI) | 96.1% | Not measured | Only o3 measured |
| MedCode (Vals AI) | 47.3% | Not measured | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | 85.3% | Not measured | Only o3 measured |
| SimpleQA Verified | 49.4% | Not measured | Only o3 measured |
| GeoBench | 74.0% | Not measured | Only o3 measured |
| LMArena Text: science | 1429 | Not measured | Only o3 measured |
| LMArena Text: medicine and healthcare | 1448 | Not measured | Only o3 measured |
| LMArena Text: legal and government | 1428 | Not measured | Only o3 measured |
| LMArena Text: business and finance | 1393 | Not measured | Only o3 measured |
| FACTS Benchmark Suite | 44.4% | Not measured | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | Not measured | Only o3 measured |
| Design Arena: UI components | 1017 | Not measured | Only o3 measured |
| Design Arena: game dev | 1034 | Not measured | Only o3 measured |
| Design Arena: data viz | 1200 | Not measured | Only o3 measured |
| Design Arena: all code categories | 1020 | Not measured | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | o3 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.8% | Not measured | Only o3 measured |
| MedScribe (Vals AI) | 76.7% | Not measured | Only o3 measured |
| IFBench | 71.4% | 64.4% | o3 |
| Long context reasoning (AA-LCR) | 74.7% | 71.7% | o3 |
| DTBench | 84.8% | 73.9% | o3 |
| LMCA | 39.7% | 29.7% | o3 |
| Fiction.LiveBench | 88.9% | Not measured | Only o3 measured |
| Creative writing (Lech Mazur) | 83.9% | Not measured | Only o3 measured |
| CL-bench | 17.8% | Not measured | Only o3 measured |
| Surface Evolver Bench | Not measured | 44.4% | Only Qwen3.6 35B A3B measured |
| LMArena Text | 1410 | Not measured | Only o3 measured |
| LMArena Text: instruction following | 1368 | Not measured | Only o3 measured |
| LMArena Text: creative writing | 1359 | Not measured | Only o3 measured |
| LMArena Vision | 1214 | Not measured | Only o3 measured |
| LMArena Text: English | 1417 | Not measured | Only o3 measured |
| LMArena Text: non-English | 1401 | Not measured | Only o3 measured |
| LMArena Text: Chinese | 1437 | Not measured | Only o3 measured |
| LMArena Text: German | 1420 | Not measured | Only o3 measured |
| LMArena Text: Japanese | 1403 | Not measured | Only o3 measured |
| LMArena Text: Korean | 1370 | Not measured | Only o3 measured |
| LMArena Text: French | 1430 | Not measured | Only o3 measured |
| LMArena Text: Spanish | 1395 | Not measured | Only o3 measured |
| LMArena Text: Russian | 1406 | Not measured | Only o3 measured |
| LMArena Text: Polish | 1438 | Not measured | Only o3 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only o3 measured |
| LMArena Text: longer queries | 1372 | Not measured | Only o3 measured |
| LMArena Text: writing and language | 1372 | Not measured | Only o3 measured |
| LMArena Text: entertainment and media | 1373 | Not measured | Only o3 measured |
| LMArena Vision: English | 1221 | Not measured | Only o3 measured |
| LMArena Vision: Chinese | 1237 | Not measured | Only o3 measured |
| LMArena Vision: reading text | 1218 | Not measured | Only o3 measured |
| LMArena Vision: humor | 1222 | Not measured | Only o3 measured |
| LMArena Vision: captioning | 1210 | Not measured | Only o3 measured |
| LMArena Vision: recognizing things | 1234 | Not measured | Only o3 measured |
| LMArena Vision: creative writing | 1201 | Not measured | Only o3 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 | 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
Qwen3.6 35B A3B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 35B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 35B 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 o3 | Qwen3.6 35B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2025 | April 27, 2026 |
| Context window | 200K | 262K |
| Input $/Mtok | $2.00 | $0.10 |
| Output $/Mtok | $8.00 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.571 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3.6 35B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o3 for work measured by τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, o3 or Qwen3.6 35B A3B?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for o3 only.
Which has the bigger context window, o3 or Qwen3.6 35B A3B?
Qwen3.6 35B A3B, with 262K tokens against 200K.
How many benchmarks is the o3 vs Qwen3.6 35B A3B comparison based on?
15 benchmarks have a result for both models as of October 21, 2026. o3'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.