GPT-6 Sol vs Qwen3.5-Flash
GPT-6 Sol leads Qwen3.5-Flash on coding, 76.0 to 41.1 on the Vibe Coding Index, and Qwen3.5-Flash costs 38.5x less per output token ($0.26 vs $10.00 per million). Updated October 22, 2026; 44 benchmarks have a result for both.
GPT-6 Sol or Qwen3.5-Flash: which to pick
Pick GPT-6 Sol for coding: it scores 76.0 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash.
Pick Qwen3.5-Flash for output tokens 38.5x cheaper ($0.26 vs $10.00 per million) and work measured by LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 44
- GPT-6 Sol only
- 60
- Qwen3.5-Flash only
- 11
- 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-6 Sol
GPT-6 Sol scores 76.0 against 41.1.
5 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-6 Sol has a AA Intelligence Index result, 47.6.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6 Sol has a Design Arena: websites result, 1293.
Directional only
Long documents
Listed context window
GPT-6 Sol
GPT-6 Sol takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-6 Sol $0.0070, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- GPT-6 Sol 76.0 from 5 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Coding, 43.0 points apart on average over 6 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-6 Sol 2462.0 against Qwen3.5-Flash 265.9, 73.2 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Sol $10.00 against Qwen3.5-Flash $0.26 per million output tokens, 38.5x apart.
- Context window
- GPT-6 Sol 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-6 Sol | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 2 | 4 | ||
| General | 14 | 7 | ||
| All shared boards | 33 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench coding | 93.1% | Not measured | Only GPT-6 Sol measured |
| LMArena WebDev | 1683 | 1244 | GPT-6 Sol |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| Terminal-Bench 4 (Vals AI) | 44.4% | Not measured | Only GPT-6 Sol measured |
| Vibe Code Bench (Vals AI) | 87.8% | Not measured | Only GPT-6 Sol measured |
| Code migration (Vals AI) | 57.2% | Not measured | Only GPT-6 Sol measured |
| ProgramBench (Vals AI) | 2.0% | Not measured | Only GPT-6 Sol measured |
| IOI (Vals AI) | 82.6% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 83.2% | Not measured | Only GPT-6 Sol measured |
| CyberBench (Vals AI) | 78.0% | Not measured | Only GPT-6 Sol measured |
| SciCode | 57.6% | Not measured | Only GPT-6 Sol measured |
| FrontierCode | 49.3% | Not measured | Only GPT-6 Sol measured |
| ALE-Bench | 2462.0 | 265.9 | GPT-6 Sol |
| LMArena Text: coding | 1448 | 1412 | GPT-6 Sol |
| LMArena Text: software and IT | 1433 | 1416 | GPT-6 Sol |
| LMArena WebDev: React | 1685 | 1229 | GPT-6 Sol |
| LMArena WebDev: HTML | 1685 | 1291 | GPT-6 Sol |
| LMArena WebDev: from an image | 1660 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.6 | Not measured | Only GPT-6 Sol measured |
| Epoch Capabilities Index | 163.7 | 143.5 | GPT-6 Sol |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1231 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | 1237 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | 1217 | Not measured | Only GPT-6 Sol measured |
| LiveBench research | 69.3% | Not measured | Only GPT-6 Sol measured |
| LiveBench browser | 92.0% | Not measured | Only GPT-6 Sol measured |
| LiveBench long agentic | 66.9% | Not measured | Only GPT-6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | 49.0% | Not measured | Only GPT-6 Sol measured |
| Tax Agent Bench (Vals AI) | 15.0% | Not measured | Only GPT-6 Sol measured |
| Legal Research Bench (Vals AI) | 28.9% | Not measured | Only GPT-6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 71.5% | Not measured | Only GPT-6 Sol measured |
| BioMysteryBench (Vals AI) | 74.8% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 30.0% | Not measured | Only GPT-6 Sol measured |
| Vals Science Index | 45.0% | Not measured | Only GPT-6 Sol measured |
| APEX-Agents | 54.3% | Not measured | Only GPT-6 Sol measured |
| SpireClimb | 7.5% | Not measured | Only GPT-6 Sol measured |
| Vending-Bench 2 | $14,428 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| LiveBench reasoning | 88.7% | Not measured | Only GPT-6 Sol measured |
| MysteryMechanism (Vals AI) | 30.2% | Not measured | Only GPT-6 Sol measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | Not measured | Only GPT-6 Sol measured |
| GPQA Diamond (Epoch AI run) | 94.3% | 82.3% | GPT-6 Sol |
| ARC-AGI | 95.5% | Not measured | Only GPT-6 Sol measured |
| ARC-AGI-2 | 89.6% | Not measured | Only GPT-6 Sol measured |
| Chess puzzles | 56.0% | 14.0% | GPT-6 Sol |
| Mystery game puzzles | 56.0% | 20.0% | GPT-6 Sol |
| EBR-bench | 53.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | 1422 | 1403 | GPT-6 Sol |
| LMArena Text: expert | 1437 | 1407 | GPT-6 Sol |
| LMArena Text: hard prompts in English | 1420 | 1409 | GPT-6 Sol |
| LMArena Vision: diagrams | 1270 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: homework | 1286 | Not measured | Only GPT-6 Sol measured |
| Humanity's Last Exam (Scale run) | 33.8% | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | 100.0% | 84.4% | GPT-6 Sol |
| FrontierMath, tiers 1 to 3 | 89.8% | 18.2% | GPT-6 Sol |
| FrontierMath, tier 4 | 90.0% | Not measured | Only GPT-6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: math | 1407 | 1407 | GPT-6 Sol |
| LMArena Text: mathematical work | 1424 | 1413 | GPT-6 Sol |
| MathArena | 80.1% | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | 47.1% | 33.0% | GPT-6 Sol |
| SimpleQA Verified | 60.7% | 20.3% | GPT-6 Sol |
| LMArena Text: science | 1389 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1366 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1396 | 1398 | Qwen3.5-Flash |
| LMArena Text: business and finance | 1380 | 1394 | Qwen3.5-Flash |
| FACTS Benchmark Suite | 72.5% | Not measured | Only GPT-6 Sol measured |
| PRBench Legal | 38.6% | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1293 | Not measured | Only GPT-6 Sol measured |
| Design Arena: UI components | 1291 | Not measured | Only GPT-6 Sol measured |
| Design Arena: game dev | 1311 | Not measured | Only GPT-6 Sol measured |
| Design Arena: data viz | 1271 | Not measured | Only GPT-6 Sol measured |
| Design Arena: 3D | 1314 | Not measured | Only GPT-6 Sol measured |
| Design Arena: all code categories | 1300 | Not measured | Only GPT-6 Sol measured |
| Design Arena: SVG | 1313 | Not measured | Only GPT-6 Sol measured |
| Design Arena: ASCII art | 1222 | Not measured | Only GPT-6 Sol measured |
| LiveBench design | 82.2% | Not measured | Only GPT-6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index147
| Benchmark | GPT-6 Sol | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.2% | Not measured | Only GPT-6 Sol measured |
| Public Benefits Bench (Vals AI) | 56.6% | Not measured | Only GPT-6 Sol measured |
| SAGE (Vals AI) | 44.8% | 42.5% | GPT-6 Sol |
| MedScribe (Vals AI) | 82.0% | 70.6% | GPT-6 Sol |
| Long context reasoning (AA-LCR) | 83.7% | Not measured | Only GPT-6 Sol measured |
| DTBench | 97.3% | 82.9% | GPT-6 Sol |
| LMCA | 59.1% | 29.1% | GPT-6 Sol |
| Furniture assembly | 58.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text | 1398 | 1397 | GPT-6 Sol |
| LMArena Text: instruction following | 1413 | 1374 | GPT-6 Sol |
| LMArena Text: creative writing | 1378 | 1343 | GPT-6 Sol |
| LMArena Vision | 1240 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: English | 1396 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1388 | 1385 | GPT-6 Sol |
| LMArena Text: Chinese | 1412 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1379 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | 1377 | 1369 | GPT-6 Sol |
| LMArena Text: Korean | 1336 | 1344 | Qwen3.5-Flash |
| LMArena Text: French | 1399 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1387 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1402 | 1380 | GPT-6 Sol |
| LMArena Text: Polish | 1372 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1414 | 1393 | GPT-6 Sol |
| LMArena Text: longer queries | 1416 | 1392 | GPT-6 Sol |
| LMArena Text: writing and language | 1389 | 1369 | GPT-6 Sol |
| LMArena Text: entertainment and media | 1373 | 1347 | GPT-6 Sol |
| LMArena Vision: English | 1229 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: reading text | 1258 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: creative writing | 1219 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Sol | 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-6 Sol | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | September 22, 2026 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $2.00 | $0.065 |
| Output $/Mtok | $10.00 | $0.26 |
| Coding index | 76.0 | 41.1 |
| Independent measurements behind it | 5 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Sol better than Qwen3.5-Flash for coding?
Pick GPT-6 Sol for coding: it scores 76.0 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash. Pick Qwen3.5-Flash for output tokens 38.5x cheaper ($0.26 vs $10.00 per million) and work measured by LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GPT-6 Sol or Qwen3.5-Flash?
GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, GPT-6 Sol or Qwen3.5-Flash?
GPT-6 Sol, with 1.05M tokens against 1M.
How many benchmarks is the GPT-6 Sol vs Qwen3.5-Flash comparison based on?
44 benchmarks have a result for both models as of October 22, 2026. GPT-6 Sol'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.