o1 vs Qwen3.8 27B
Qwen3.8 27B leads o1 on the Artificial Analysis Intelligence Index, 33.7 to 15.2, and Qwen3.8 27B costs 23.5x less per output token ($2.55 vs $60.00 per million). Updated October 22, 2026; 45 benchmarks have a result for both.
o1 or Qwen3.8 27B: which to pick
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.
Pick o1 for work measured by TaxEval (Vals AI) or LMArena Text: Korean, where it leads.
- Shared results
- 45
- o1 only
- 24
- Qwen3.8 27B only
- 40
- 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
Only Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 15.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 73.2%.
Same board, both measured
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 27B
Qwen3.8 27B leads, 84.3% against 83.5%.
Same board, both measured
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 27B
o1 $0.045, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- o1 not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
- Widest category
- Coding, 25.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- ARC-AGI: o1 30.7% against Qwen3.8 27B 87.5%, 56.8 points apart on a 0 to 100 scale.
- Output price
- o1 $60.00 against Qwen3.8 27B $2.55 per million output tokens, 23.5x apart.
- Context window
- o1 takes 200K tokens, Qwen3.8 27B 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 | o1 | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 8 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 6 | ||
| General | 1 | 21 | ||
| All shared boards | 2 | 43 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| Aider polyglot | 61.7% | Not measured | Only o1 measured |
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1593 | Only Qwen3.8 27B measured |
| AA Coding Index | 39.7 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | 50.3% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| LiveCodeBench (Artificial Analysis run) | 67.9% | Not measured | Only o1 measured |
| SciCode | Not measured | 46.6% | Only Qwen3.8 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 12.9% | Not measured | Only o1 measured |
| WeirdML | 46.1% | Not measured | Only o1 measured |
| LMArena Text: coding | 1367 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1366 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | Not measured | 1602 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1570 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| SciCode | 4.6% | Not measured | Only o1 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.2 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | 141.7 | 151.7 | Qwen3.8 27B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 62.6% | Not measured | Only o1 measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| METR time horizons | 51.1% | Not measured | Only o1 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 73.2% | 88.9% | Qwen3.8 27B |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | 77.4% | 83.9% | Qwen3.8 27B |
| GPQA Diamond (Artificial Analysis run) | 74.7% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 33.9% | Qwen3.8 27B |
| GPQA Diamond (Epoch AI run) | 76.8% | Not measured | Only o1 measured |
| ARC-AGI | 30.7% | 87.5% | Qwen3.8 27B |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| SimpleBench | 40.1% | Not measured | Only o1 measured |
| Chess puzzles | 10.0% | Not measured | Only o1 measured |
| VPCT | 37.0% | Not measured | Only o1 measured |
| LMArena Text: hard prompts | 1371 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1361 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1372 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AIME | 71.5% | Not measured | Only o1 measured |
| MATH 500 (Vals AI) | 90.4% | Not measured | Only o1 measured |
| MGSM (Vals AI) | 89.3% | Not measured | Only o1 measured |
| OTIS Mock AIME | 73.3% | Not measured | Only o1 measured |
| FrontierMath, tiers 1 to 3 | 14.7% | Not measured | Only o1 measured |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| MATH level 5 | 94.7% | Not measured | Only o1 measured |
| LMArena Text: math | 1388 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1390 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 83.5% | 84.3% | Qwen3.8 27B |
| LegalBench (Vals AI) | 80.4% | 82.4% | Qwen3.8 27B |
| TaxEval (Vals AI) | 74.3% | 70.8% | o1 |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedQA (Vals AI) | 96.5% | Not measured | Only o1 measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| MMLU Pro (Artificial Analysis run) | 84.1% | Not measured | Only o1 measured |
| SimpleQA Verified | 41.1% | Not measured | Only o1 measured |
| GeoBench | 80.0% | Not measured | Only o1 measured |
| LMArena Text: science | 1369 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1355 | 1437 | Qwen3.8 27B |
| LMArena Text: legal and government | 1368 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1328 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | o1 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| IFBench | 70.3% | Not measured | Only o1 measured |
| Long context reasoning (AA-LCR) | 65.0% | 82.0% | Qwen3.8 27B |
| DTBench | 74.7% | 88.0% | Qwen3.8 27B |
| LMCA | 22.3% | 41.4% | Qwen3.8 27B |
| Fiction.LiveBench | 83.3% | Not measured | Only o1 measured |
| Creative writing (Lech Mazur) | 70.2% | Not measured | Only o1 measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | 1366 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1367 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1347 | 1383 | Qwen3.8 27B |
| LMArena Vision | 1168 | 1269 | Qwen3.8 27B |
| LMArena Text: English | 1372 | 1448 | Qwen3.8 27B |
| LMArena Text: non-English | 1358 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1394 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1337 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | 1346 | 1387 | Qwen3.8 27B |
| LMArena Text: Korean | 1396 | 1384 | o1 |
| LMArena Text: French | 1344 | 1465 | Qwen3.8 27B |
| LMArena Text: Spanish | 1345 | 1449 | Qwen3.8 27B |
| LMArena Text: Russian | 1356 | 1415 | Qwen3.8 27B |
| LMArena Text: Polish | Not measured | 1439 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | 1355 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1378 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1360 | 1405 | Qwen3.8 27B |
| LMArena Text: entertainment and media | 1358 | 1393 | Qwen3.8 27B |
| LMArena Vision: English | 1162 | 1267 | Qwen3.8 27B |
| LMArena Vision: Chinese | 1235 | 1326 | Qwen3.8 27B |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1 | Qwen3.8 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
Qwen3.8 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 o1 | Qwen3.8 27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 17, 2024 | August 14, 2026 |
| Context window | 200K | 1M |
| Input $/Mtok | $15.00 | $0.425 |
| Output $/Mtok | $60.00 | $2.55 |
| Coding index | Not benchmarked | 74.2 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o1 better than Qwen3.8 27B for coding?
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for work measured by TaxEval (Vals AI) or LMArena Text: Korean, where it leads.
Which is cheaper, o1 or Qwen3.8 27B?
o1 costs $15.00 per million input tokens and $60.00 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, o1 or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 200K.
How many benchmarks is the o1 vs Qwen3.8 27B comparison based on?
45 benchmarks have a result for both models as of October 22, 2026. o1's coding index rests on 0 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.