o3 vs Qwen3.5-27B
Qwen3.5-27B leads o3 on the Artificial Analysis Intelligence Index, 22.9 to 20.2, and Qwen3.5-27B costs 5.1x less per output token ($1.56 vs $8.00 per million). Updated October 21, 2026; 49 benchmarks have a result for both.
o3 or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick o3 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 49
- o3 only
- 53
- Qwen3.5-27B only
- 4
- Like-for-like categories
- 7 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 20.2.
Same board, both measured
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
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
o3 $0.0060, Qwen3.5-27B $0.0010 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, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 13.2 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: o3 933.5 against Qwen3.5-27B 349.4, 19.5 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3.5-27B $1.56 per million output tokens, 5.1x apart.
- Context window
- o3 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 | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 3 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 2 | ||
| General | 13 | 16 | ||
| All shared boards | 18 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
| Benchmark | o3 | Qwen3.5-27B | 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 |
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| LiveCodeBench (Vals AI) | 83.9% | Not measured | Only o3 measured |
| LiveCodeBench (Artificial Analysis run) | 80.8% | Not measured | Only o3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 32.6% | o3 |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | 39.5% | o3 |
| ALE-Bench | 933.5 | 349.4 | o3 |
| LMArena Text: coding | 1407 | 1427 | Qwen3.5-27B |
| LMArena Text: software and IT | 1415 | 1431 | Qwen3.5-27B |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B 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 index01
| Benchmark | o3 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 146.7 | Not measured | Only o3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | o3 | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 80.7% | 93.9% | Qwen3.5-27B |
| 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 |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B 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 index07
| Benchmark | o3 | Qwen3.5-27B | 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% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | 23.9% | Qwen3.5-27B |
| GPQA Diamond (Epoch AI run) | 81.8% | Not measured | Only o3 measured |
| 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% | Not measured | Only o3 measured |
| VPCT | 52.0% | Not measured | Only o3 measured |
| LMArena Text: hard prompts | 1402 | 1415 | Qwen3.5-27B |
| LMArena Text: expert | 1402 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1411 | 1429 | Qwen3.5-27B |
| LMArena Vision: diagrams | 1220 | 1249 | Qwen3.5-27B |
| LMArena Vision: homework | 1234 | 1256 | Qwen3.5-27B |
| 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 index02
| Benchmark | o3 | Qwen3.5-27B | 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% | Not measured | Only o3 measured |
| FrontierMath, tiers 1 to 3 | 33.3% | Not measured | Only o3 measured |
| MATH level 5 | 97.8% | Not measured | Only o3 measured |
| LMArena Text: math | 1426 | 1429 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1423 | 1429 | Qwen3.5-27B |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | o3 | Qwen3.5-27B | 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 | 1437 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1448 | 1425 | o3 |
| LMArena Text: legal and government | 1428 | 1405 | o3 |
| LMArena Text: business and finance | 1393 | 1399 | Qwen3.5-27B |
| 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 | Qwen3.5-27B | 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 index1316
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 | 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 | Qwen3.5-27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2025 | February 25, 2026 |
| Context window | 200K | 262K |
| Input $/Mtok | $2.00 | $0.195 |
| Output $/Mtok | $8.00 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | $0.571 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o3 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, o3 or Qwen3.5-27B?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for o3 only.
Which has the bigger context window, o3 or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 200K.
How many benchmarks is the o3 vs Qwen3.5-27B comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. o3's coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.