o3 vs Qwen3.6 27B
Updated October 21, 2026. 18 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.
o3 has the edge where a solved task costs $0.571 against $1.30, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
- Shared results
- 18
- o3 only
- 79
- Qwen3.6 27B only
- 6
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Only Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 20.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
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 27B
Qwen3.6 27B takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
o3 $0.0060, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
o3
o3 $0.571, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- o3 not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Agentic work, 13.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Mystery game puzzles: o3 29.0% against Qwen3.6 27B 7.0%, 22.0 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3.6 27B $2.70 per million output tokens, 3.0x apart.
- Context window
- o3 takes 200K tokens, Qwen3.6 27B 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 27B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 2 | ||
| General | 3 | 2 | ||
| All shared boards | 7 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | o3 | Qwen3.6 27B | 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 | 53.7 | Only Qwen3.6 27B measured |
| LiveCodeBench (Vals AI) | 83.9% | Not measured | Only o3 measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| LiveCodeBench (Artificial Analysis run) | 80.8% | Not measured | Only o3 measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 34.8% | o3 |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | Not measured | Only o3 measured |
| 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 |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | o3 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 146.7 | 146.2 | o3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | 80.7% | 94.2% | Qwen3.6 27B |
| 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 |
| 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 27B | 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% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | 81.8% | 85.9% | Qwen3.6 27B |
| 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% | 7.0% | 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 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | o3 | Qwen3.6 27B | 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% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | 33.3% | 35.1% | Qwen3.6 27B |
| 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 | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | o3 | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMLU Pro | 85.6% | Not measured | Only o3 measured |
| LegalBench (Vals AI) | 83.8% | Not measured | Only o3 measured |
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 59.7% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 74.6% | 71.3% | o3 |
| MortgageTax (Vals AI) | 65.7% | 68.3% | Qwen3.6 27B |
| 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 |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | o3 | Qwen3.6 27B | 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 index32
| Benchmark | o3 | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.8% | 45.6% | Qwen3.6 27B |
| MedScribe (Vals AI) | 76.7% | Not measured | Only o3 measured |
| IFBench | 71.4% | 67.6% | o3 |
| Long context reasoning (AA-LCR) | 74.7% | 77.3% | Qwen3.6 27B |
| DTBench | 84.8% | 78.1% | o3 |
| LMCA | 39.7% | 34.5% | 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 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 | Qwen3.6 27B | 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 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 27B 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 27B |
|---|---|---|
| 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 | $0.45 |
| Output $/Mtok | $8.00 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | $0.571 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. o3 has the edge where a solved task costs $0.571 against $1.30, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
Which is cheaper, o3 or Qwen3.6 27B?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, o3 costs $0.571 and Qwen3.6 27B $1.30.
Which has the bigger context window, o3 or Qwen3.6 27B?
Qwen3.6 27B, with 262K tokens against 200K.
How many benchmarks is the o3 vs Qwen3.6 27B comparison based on?
18 benchmarks have a result for both models as of October 21, 2026. o3's coding index rests on 0 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.