o3 Pro vs Qwen3.5-27B
Updated October 21, 2026. 5 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o3 Pro for coding: it scores 84.9 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.
Pick Qwen3.5-27B when output tokens cost 37.0x less, or when the context window is larger, 262K against 200K tokens.
- Shared results
- 5
- o3 Pro only
- 6
- Qwen3.5-27B only
- 48
- Like-for-like categories
- 4 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, four public coding benchmarks
o3 Pro
o3 Pro scores 84.9 against 22.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 21.9.
Same board, both measured
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
o3 Pro $0.060, Qwen3.5-27B $0.0014 per turn.
List prices
At a glance
- Coding index
- o3 Pro 84.9 from 1 benchmark, Qwen3.5-27B 22.6 from 1 benchmark.
- Widest category
- Coding, 18.7 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: o3 Pro 58.2% against Qwen3.5-27B 39.5%, 18.7 points apart on a 0 to 100 scale.
- Output price
- o3 Pro $80.00 against Qwen3.5-27B $2.16 per million output tokens, 37.0x apart.
- Context window
- o3 Pro takes 200K tokens, Qwen3.5-27B 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 Pro | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 2 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index10
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| Aider polyglot | 84.9% | Not measured | Only o3 Pro measured |
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Qwen3.5-27B measured |
| WeirdML | 58.2% | 39.5% | o3 Pro |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: software and IT | Not measured | 1431 | Only Qwen3.5-27B measured |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.9 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 147.3 | Not measured | Only o3 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.9% | Only Qwen3.5-27B measured |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.5% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.9% | Only Qwen3.5-27B measured |
| ARC-AGI | 59.3% | Not measured | Only o3 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only o3 Pro measured |
| LMArena Text: hard prompts | Not measured | 1415 | Only Qwen3.5-27B measured |
| LMArena Text: expert | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts in English | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Text: mathematical work | Not measured | 1429 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1437 | Only Qwen3.5-27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: legal and government | Not measured | 1405 | Only Qwen3.5-27B measured |
| LMArena Text: business and finance | Not measured | 1399 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | o3 Pro | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.6% | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.7% | Only Qwen3.5-27B measured |
| DTBench | 86.9% | 82.4% | o3 Pro |
| LMCA | 38.5% | 34.0% | o3 Pro |
| Fiction.LiveBench | 97.2% | Not measured | Only o3 Pro measured |
| Creative writing (Lech Mazur) | 84.4% | Not measured | Only o3 Pro measured |
| LMArena Text | Not measured | 1409 | Only Qwen3.5-27B measured |
| LMArena Text: instruction following | Not measured | 1393 | Only Qwen3.5-27B measured |
| LMArena Text: creative writing | Not measured | 1362 | Only Qwen3.5-27B measured |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: non-English | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Chinese | Not measured | 1478 | Only Qwen3.5-27B measured |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: Spanish | Not measured | 1407 | Only Qwen3.5-27B measured |
| LMArena Text: Russian | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: longer queries | Not measured | 1413 | Only Qwen3.5-27B measured |
| LMArena Text: writing and language | Not measured | 1380 | Only Qwen3.5-27B measured |
| LMArena Text: entertainment and media | Not measured | 1363 | Only Qwen3.5-27B measured |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 Pro | Qwen3.5-27B | 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
Qwen3.5-27B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 Pro | Qwen3.5-27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | June 10, 2025 | February 25, 2026 |
| Context window | 200K | 262K |
| Input $/Mtok | $20.00 | $0.27 |
| Output $/Mtok | $80.00 | $2.16 |
| Coding index | 84.9 | 22.6 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 Pro better than Qwen3.5-27B for coding?
Pick o3 Pro for coding: it scores 84.9 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when output tokens cost 37.0x less, or when the context window is larger, 262K against 200K tokens.
Which is cheaper, o3 Pro or Qwen3.5-27B?
o3 Pro costs $20.00 per million input tokens and $80.00 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for neither of them.
Which has the bigger context window, o3 Pro or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 200K.
How many benchmarks is the o3 Pro vs Qwen3.5-27B comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. o3 Pro's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.