GPT-5.5 vs Qwen3.5-Flash
GPT-5.5 leads Qwen3.5-Flash on coding, 71.3 to 41.1 on the Vibe Coding Index, and Qwen3.5-Flash costs 115.4x less per output token ($0.26 vs $30.00 per million). Updated October 22, 2026; 54 benchmarks have a result for both.
GPT-5.5 or Qwen3.5-Flash: which to pick
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash.
Pick Qwen3.5-Flash for output tokens 115.4x cheaper ($0.26 vs $30.00 per million) and a lower cost per solved task ($0.40 against $1.65).
- Shared results
- 54
- GPT-5.5 only
- 80
- Qwen3.5-Flash only
- 1
- 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
GPT-5.5
GPT-5.5 scores 71.3 against 41.1.
5 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.5 has a AA Intelligence Index result, 38.4.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 82.8%.
Same board, both measured
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 84.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.5 has a Design Arena: websites result, 1256.
Directional only
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-5.5 $0.020, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.5-Flash
GPT-5.5 $1.65, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.5 71.3 from 5 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Coding, 28.6 points apart on average over 8 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: GPT-5.5 85.3% against Qwen3.5-Flash 18.2%, 67.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 $30.00 against Qwen3.5-Flash $0.26 per million output tokens, 115.4x apart.
- Context window
- GPT-5.5 takes 1.05M 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 | GPT-5.5 | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 3 | 0 | 1 tie | |
| Knowledge | 12 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 53 | 0 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | 1244 | GPT-5.5 |
| AA Coding Index | 74.9 | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | 85.3% | 83.3% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.6% | 64.4% | GPT-5.5 |
| Vibe Code Bench (Vals AI) | 69.8% | Not measured | Only GPT-5.5 measured |
| Code migration (Vals AI) | 45.2% | Not measured | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | Not measured | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | 62.5% | Not measured | Only GPT-5.5 measured |
| SciCode | 56.1% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | Not measured | Only GPT-5.5 measured |
| GSO-Bench | 40.2% | Not measured | Only GPT-5.5 measured |
| WeirdML | 84.9% | Not measured | Only GPT-5.5 measured |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | 265.9 | GPT-5.5 |
| LMArena Text: coding | 1494 | 1412 | GPT-5.5 |
| LMArena Text: software and IT | 1493 | 1416 | GPT-5.5 |
| LMArena WebDev: React | 1506 | 1229 | GPT-5.5 |
| LMArena WebDev: HTML | 1543 | 1291 | GPT-5.5 |
| LMArena WebDev: from an image | 1532 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | Not measured | Only GPT-5.5 measured |
| Epoch Capabilities Index | 159.7 | 143.5 | GPT-5.5 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building web apps | 1117 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | 1089 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | 1142 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | 1164 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1173 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building games | 1179 | Not measured | Only GPT-5.5 measured |
| Design Arena: HTML slides | 1017 | Not measured | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | 1084 | Not measured | Only GPT-5.5 measured |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | Not measured | Only GPT-5.5 measured |
| APEX-Agents | 55.1% | Not measured | Only GPT-5.5 measured |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | Not measured | Only GPT-5.5 measured |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| Vending-Bench 2 | $7,524 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | 82.8% | GPT-5.5 |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | 81.9% | GPT-5.5 |
| GPQA Diamond (Artificial Analysis run) | 93.5% | Not measured | Only GPT-5.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | Not measured | Only GPT-5.5 measured |
| GPQA Diamond (Epoch AI run) | 90.7% | 82.3% | GPT-5.5 |
| ARC-AGI | 95.0% | Not measured | Only GPT-5.5 measured |
| ARC-AGI-2 | 85.0% | Not measured | Only GPT-5.5 measured |
| SimpleBench | 69.0% | Not measured | Only GPT-5.5 measured |
| Chess puzzles | 63.0% | 14.0% | GPT-5.5 |
| Mystery game puzzles | 56.0% | 20.0% | GPT-5.5 |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1489 | 1403 | GPT-5.5 |
| LMArena Text: expert | 1507 | 1407 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1483 | 1409 | GPT-5.5 |
| LMArena Vision: diagrams | 1325 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: homework | 1332 | Not measured | Only GPT-5.5 measured |
| Kagi LLM Benchmark | 88.8% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | 84.4% | 84.4% | Tie |
| FrontierMath, tiers 1 to 3 | 85.3% | 18.2% | GPT-5.5 |
| FrontierMath, tier 4 | 72.5% | Not measured | Only GPT-5.5 measured |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: math | 1487 | 1407 | GPT-5.5 |
| LMArena Text: mathematical work | 1496 | 1413 | GPT-5.5 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | 84.1% | GPT-5.5 |
| LegalBench (Vals AI) | 86.5% | 84.3% | GPT-5.5 |
| CaseLaw (Vals AI) | 66.2% | 56.0% | GPT-5.5 |
| CorpFin (Vals AI) | 68.4% | 63.6% | GPT-5.5 |
| TaxEval (Vals AI) | 75.0% | 72.2% | GPT-5.5 |
| MortgageTax (Vals AI) | 68.8% | 67.4% | GPT-5.5 |
| MedCode (Vals AI) | 49.1% | 33.0% | GPT-5.5 |
| SimpleQA Verified | 63.0% | 20.3% | GPT-5.5 |
| LMArena Text: science | 1485 | 1415 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1465 | 1407 | GPT-5.5 |
| LMArena Text: legal and government | 1491 | 1398 | GPT-5.5 |
| LMArena Text: business and finance | 1478 | 1394 | GPT-5.5 |
| FACTS Benchmark Suite | 66.7% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | Not measured | Only GPT-5.5 measured |
| Design Arena: UI components | 1257 | Not measured | Only GPT-5.5 measured |
| Design Arena: game dev | 1293 | Not measured | Only GPT-5.5 measured |
| Design Arena: data viz | 1264 | Not measured | Only GPT-5.5 measured |
| Design Arena: 3D | 1197 | Not measured | Only GPT-5.5 measured |
| Design Arena: all code categories | 1260 | Not measured | Only GPT-5.5 measured |
| Design Arena: SVG | 1234 | Not measured | Only GPT-5.5 measured |
| Design Arena: ASCII art | 1255 | Not measured | Only GPT-5.5 measured |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GPT-5.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | 42.5% | GPT-5.5 |
| MedScribe (Vals AI) | 86.9% | 70.6% | GPT-5.5 |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | Not measured | Only GPT-5.5 measured |
| Long context reasoning (AA-LCR) | 84.3% | Not measured | Only GPT-5.5 measured |
| DTBench | 96.0% | 82.9% | GPT-5.5 |
| LMCA | 54.3% | 29.1% | GPT-5.5 |
| Surface Evolver Bench | 88.1% | Not measured | Only GPT-5.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | 1397 | GPT-5.5 |
| LMArena Text: instruction following | 1478 | 1374 | GPT-5.5 |
| LMArena Text: creative writing | 1455 | 1343 | GPT-5.5 |
| LMArena Vision | 1297 | Not measured | Only GPT-5.5 measured |
| LMArena Text: English | 1470 | 1404 | GPT-5.5 |
| LMArena Text: non-English | 1467 | 1385 | GPT-5.5 |
| LMArena Text: Chinese | 1534 | 1445 | GPT-5.5 |
| LMArena Text: German | 1480 | 1390 | GPT-5.5 |
| LMArena Text: Japanese | 1499 | 1369 | GPT-5.5 |
| LMArena Text: Korean | 1460 | 1344 | GPT-5.5 |
| LMArena Text: French | 1485 | 1413 | GPT-5.5 |
| LMArena Text: Spanish | 1468 | 1400 | GPT-5.5 |
| LMArena Text: Russian | 1473 | 1380 | GPT-5.5 |
| LMArena Text: Polish | 1465 | 1376 | GPT-5.5 |
| LMArena Text: multi-turn | 1476 | 1393 | GPT-5.5 |
| LMArena Text: longer queries | 1484 | 1392 | GPT-5.5 |
| LMArena Text: writing and language | 1466 | 1369 | GPT-5.5 |
| LMArena Text: entertainment and media | 1449 | 1347 | GPT-5.5 |
| LMArena Vision: English | 1292 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: Chinese | 1360 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: reading text | 1306 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: humor | 1293 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | 1231 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-5.5 measured |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 | 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 GPT-5.5 | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $5.00 | $0.065 |
| Output $/Mtok | $30.00 | $0.26 |
| Coding index | 71.3 | 41.1 |
| Independent measurements behind it | 5 | 2 |
| Cost per solved task | $1.65 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than Qwen3.5-Flash for coding?
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash. Pick Qwen3.5-Flash for output tokens 115.4x cheaper ($0.26 vs $30.00 per million) and a lower cost per solved task ($0.40 against $1.65).
Which is cheaper, GPT-5.5 or Qwen3.5-Flash?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-5.5 costs $1.65 and Qwen3.5-Flash $0.40.
Which has the bigger context window, GPT-5.5 or Qwen3.5-Flash?
GPT-5.5, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.5 vs Qwen3.5-Flash comparison based on?
54 benchmarks have a result for both models as of October 22, 2026. GPT-5.5's coding index rests on 5 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.