o3 vs Qwen3.5-Flash
Qwen3.5-Flash costs 30.8x less per output token than o3 ($0.26 vs $8.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
o3 or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.
Pick o3 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
- Shared results
- 50
- o3 only
- 52
- Qwen3.5-Flash 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
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only o3 has a AA Intelligence Index result, 20.2.
Directional only
Reasoning
GPQA Diamond
o3
o3 leads, 84.1% against 82.8%.
Same board, both measured
Math
AIME
Qwen3.5-Flash
Qwen3.5-Flash leads, 92.5% against 85.3%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 84.1%.
Same board, both measured
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-Flash
Qwen3.5-Flash takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
o3 $0.0060, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.5-Flash
o3 $0.571, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- o3 not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Knowledge, 6.2 points apart on average over 11 shared benchmarks.
- Widest single gap
- SimpleQA Verified: o3 49.4% against Qwen3.5-Flash 20.3%, 29.1 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3.5-Flash $0.26 per million output tokens, 30.8x apart.
- Context window
- o3 takes 200K tokens, Qwen3.5-Flash 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 | o3 | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 4 | ||
| Math | 3 | 1 | 1 tie | |
| Knowledge | 7 | 4 | ||
| General | 16 | 5 | ||
| All shared boards | 33 | 16 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | o3 | Qwen3.5-Flash | 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 | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | 83.9% | 83.3% | o3 |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| LiveCodeBench (Artificial Analysis run) | 80.8% | Not measured | Only o3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only o3 measured |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | Not measured | Only o3 measured |
| ALE-Bench | 933.5 | 265.9 | o3 |
| LMArena Text: coding | 1407 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1415 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| SciCode | 9.2% | 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 index10
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | Not measured | Only o3 measured |
| Epoch Capabilities Index | 146.7 | 143.5 | o3 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 80.7% | Not measured | Only o3 measured |
| 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 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 84.1% | 82.8% | o3 |
| MMMU Pro (Vals AI) | 80.4% | 81.9% | Qwen3.5-Flash |
| GPQA Diamond (Artificial Analysis run) | 82.7% | Not measured | Only o3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | Not measured | Only o3 measured |
| GPQA Diamond (Epoch AI run) | 81.8% | 82.3% | Qwen3.5-Flash |
| 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% | 14.0% | o3 |
| Mystery game puzzles | 29.0% | 20.0% | o3 |
| VPCT | 52.0% | Not measured | Only o3 measured |
| LMArena Text: hard prompts | 1402 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1402 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1411 | 1409 | o3 |
| 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 index31
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | 85.3% | 92.5% | Qwen3.5-Flash |
| 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% | 84.4% | Tie |
| FrontierMath, tiers 1 to 3 | 33.3% | 18.2% | o3 |
| MATH level 5 | 97.8% | Not measured | Only o3 measured |
| LMArena Text: math | 1426 | 1407 | o3 |
| LMArena Text: mathematical work | 1423 | 1413 | o3 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index74
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 85.6% | 84.1% | o3 |
| LegalBench (Vals AI) | 83.8% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | 59.7% | 63.6% | Qwen3.5-Flash |
| TaxEval (Vals AI) | 74.6% | 72.2% | o3 |
| MortgageTax (Vals AI) | 65.7% | 67.4% | Qwen3.5-Flash |
| MedQA (Vals AI) | 96.1% | Not measured | Only o3 measured |
| MedCode (Vals AI) | 47.3% | 33.0% | o3 |
| MMLU Pro (Artificial Analysis run) | 85.3% | Not measured | Only o3 measured |
| SimpleQA Verified | 49.4% | 20.3% | o3 |
| GeoBench | 74.0% | Not measured | Only o3 measured |
| LMArena Text: science | 1429 | 1415 | o3 |
| LMArena Text: medicine and healthcare | 1448 | 1407 | o3 |
| LMArena Text: legal and government | 1428 | 1398 | o3 |
| LMArena Text: business and finance | 1393 | 1394 | Qwen3.5-Flash |
| FACTS Benchmark Suite | 44.4% | 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 | Qwen3.5-Flash | 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 index165
| Benchmark | o3 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.8% | 42.5% | Qwen3.5-Flash |
| MedScribe (Vals AI) | 76.7% | 70.6% | o3 |
| IFBench | 71.4% | Not measured | Only o3 measured |
| Long context reasoning (AA-LCR) | 74.7% | Not measured | Only o3 measured |
| DTBench | 84.8% | 82.9% | o3 |
| LMCA | 39.7% | 29.1% | 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 | 1397 | o3 |
| LMArena Text: instruction following | 1368 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1359 | 1343 | o3 |
| LMArena Vision | 1214 | Not measured | Only o3 measured |
| LMArena Text: English | 1417 | 1404 | o3 |
| LMArena Text: non-English | 1401 | 1385 | o3 |
| LMArena Text: Chinese | 1437 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1420 | 1390 | o3 |
| LMArena Text: Japanese | 1403 | 1369 | o3 |
| LMArena Text: Korean | 1370 | 1344 | o3 |
| LMArena Text: French | 1430 | 1413 | o3 |
| LMArena Text: Spanish | 1395 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1406 | 1380 | o3 |
| LMArena Text: Polish | 1438 | 1376 | o3 |
| LMArena Text: multi-turn | 1405 | 1393 | o3 |
| LMArena Text: longer queries | 1372 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1372 | 1369 | o3 |
| LMArena Text: entertainment and media | 1373 | 1347 | o3 |
| 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 | Qwen3.5-Flash | 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-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash 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-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 16, 2025 | February 25, 2026 |
| Context window | 200K | 1M |
| Input $/Mtok | $2.00 | $0.065 |
| Output $/Mtok | $8.00 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | $0.571 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. Pick o3 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
Which is cheaper, o3 or Qwen3.5-Flash?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, o3 costs $0.571 and Qwen3.5-Flash $0.40.
Which has the bigger context window, o3 or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 200K.
How many benchmarks is the o3 vs Qwen3.5-Flash comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. o3's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.