GPT-6 Sol vs Qwen3.8 27B
Updated October 21, 2026. 70 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 73.6 for Qwen3.8 27B.
Pick Qwen3.8 27B when output tokens cost 7.4x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 70
- GPT-6 Sol only
- 30
- Qwen3.8 27B only
- 15
- Like-for-like categories
- 8 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
GPT-6 Sol
GPT-6 Sol scores 82.8 against 73.6.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-6 Sol
GPT-6 Sol leads, 47.6 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6 Sol has a Design Arena: websites result, 1292.
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
Qwen3.8 27B
GPT-6 Sol $0.0070, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- GPT-6 Sol 82.8 from 2 benchmarks, Qwen3.8 27B 73.6 from 2 benchmarks.
- Widest category
- Math, 26.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- ProofBench: GPT-6 Sol 83.0% against Qwen3.8 27B 16.0%, 67.0 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Sol $10.00 against Qwen3.8 27B $1.35 per million output tokens, 7.4x apart.
- Context window
- GPT-6 Sol takes 1.05M tokens, Qwen3.8 27B 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 | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 12 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 7 | 2 | ||
| Reasoning | 4 | 5 | ||
| Math | 1 | 2 | ||
| Knowledge | 1 | 4 | ||
| Web design | 1 | 0 | ||
| General | 4 | 23 | ||
| All shared boards | 32 | 38 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index122
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | 93.1% | 86.8% | GPT-6 Sol |
| LMArena WebDev | 1683 | 1593 | GPT-6 Sol |
| AA Coding Index | Not measured | 68.1 | Only Qwen3.8 27B measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Terminal-Bench 4 (Vals AI) | 44.4% | Not measured | Only GPT-6 Sol measured |
| Vibe Code Bench (Vals AI) | 87.8% | 64.8% | GPT-6 Sol |
| Code migration (Vals AI) | 57.2% | 14.2% | GPT-6 Sol |
| ProgramBench (Vals AI) | 2.0% | 0.0% | GPT-6 Sol |
| IOI (Vals AI) | 82.6% | 39.1% | GPT-6 Sol |
| Terminal-Bench 2.1 (Vals AI) | 83.2% | 58.4% | GPT-6 Sol |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | 78.0% | 67.8% | GPT-6 Sol |
| SciCode | 57.6% | 46.6% | GPT-6 Sol |
| FrontierCode | 49.3% | Not measured | Only GPT-6 Sol measured |
| ALE-Bench | 2462.0 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: coding | 1448 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1433 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | 1685 | 1602 | GPT-6 Sol |
| LMArena WebDev: HTML | 1685 | 1570 | GPT-6 Sol |
| LMArena WebDev: from an image | 1660 | 1577 | GPT-6 Sol |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.6 | 33.7 | GPT-6 Sol |
| Epoch Capabilities Index | 163.7 | 151.7 | GPT-6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index72
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| Design Arena: agents building web apps | 1232 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | 1238 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | 1216 | Not measured | Only GPT-6 Sol measured |
| LiveBench research | 69.3% | 59.4% | GPT-6 Sol |
| LiveBench browser | 92.0% | 32.0% | GPT-6 Sol |
| LiveBench long agentic | 66.9% | 27.9% | GPT-6 Sol |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | 49.0% | 48.5% | GPT-6 Sol |
| Tax Agent Bench (Vals AI) | 15.0% | 30.9% | Qwen3.8 27B |
| Legal Research Bench (Vals AI) | 28.9% | 36.1% | Qwen3.8 27B |
| Excel Modeling Benchmark (Vals AI) | 71.5% | 59.7% | GPT-6 Sol |
| BioMysteryBench (Vals AI) | 74.8% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 30.0% | 0.0% | GPT-6 Sol |
| Vals Science Index | 45.0% | Not measured | Only GPT-6 Sol measured |
| APEX-Agents | 54.3% | 47.5% | GPT-6 Sol |
| 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 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index45
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.9% | Only Qwen3.8 27B measured |
| LiveBench reasoning | 88.7% | 80.0% | GPT-6 Sol |
| MysteryMechanism (Vals AI) | 30.2% | Not measured | Only GPT-6 Sol measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 90.5% | Only Qwen3.8 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 33.9% | GPT-6 Sol |
| GPQA Diamond (Epoch AI run) | 94.3% | Not measured | Only GPT-6 Sol measured |
| ARC-AGI | 95.5% | 87.5% | GPT-6 Sol |
| ARC-AGI-2 | 89.6% | 42.4% | GPT-6 Sol |
| Chess puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| EBR-bench | 53.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | 1422 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1437 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1420 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | 1270 | 1285 | Qwen3.8 27B |
| LMArena Vision: homework | 1286 | 1295 | Qwen3.8 27B |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.8% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tier 4 | 90.0% | Not measured | Only GPT-6 Sol measured |
| ProofBench | 83.0% | 16.0% | GPT-6 Sol |
| LMArena Text: math | 1407 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1424 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.3% | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | Not measured | 70.8% | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | 47.1% | 28.7% | GPT-6 Sol |
| SimpleQA Verified | 60.7% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: science | 1389 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1366 | 1437 | Qwen3.8 27B |
| LMArena Text: legal and government | 1396 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1380 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1292 | Not measured | Only GPT-6 Sol measured |
| Design Arena: UI components | 1292 | Not measured | Only GPT-6 Sol measured |
| Design Arena: game dev | 1313 | Not measured | Only GPT-6 Sol measured |
| Design Arena: data viz | 1269 | Not measured | Only GPT-6 Sol measured |
| Design Arena: 3D | 1322 | Not measured | Only GPT-6 Sol measured |
| Design Arena: all code categories | 1301 | Not measured | Only GPT-6 Sol measured |
| Design Arena: SVG | 1318 | Not measured | Only GPT-6 Sol measured |
| Design Arena: ASCII art | 1226 | Not measured | Only GPT-6 Sol measured |
| LiveBench design | 82.2% | 75.9% | GPT-6 Sol |
GeneralLike-for-likePublic boards, shown as published, kept out of the index423
| Benchmark | GPT-6 Sol | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.2% | 76.6% | GPT-6 Sol |
| Public Benefits Bench (Vals AI) | 56.6% | Not measured | Only GPT-6 Sol measured |
| SAGE (Vals AI) | 44.8% | 52.4% | Qwen3.8 27B |
| MedScribe (Vals AI) | 82.0% | 83.8% | Qwen3.8 27B |
| Long context reasoning (AA-LCR) | 83.7% | 82.0% | GPT-6 Sol |
| DTBench | 97.3% | 88.0% | GPT-6 Sol |
| LMCA | 59.1% | 41.4% | GPT-6 Sol |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| Furniture assembly | 58.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text | 1398 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1413 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1378 | 1383 | Qwen3.8 27B |
| LMArena Vision | 1240 | 1269 | Qwen3.8 27B |
| LMArena Text: English | 1396 | 1448 | Qwen3.8 27B |
| LMArena Text: non-English | 1388 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1412 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1379 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | 1377 | 1387 | Qwen3.8 27B |
| LMArena Text: Korean | 1336 | 1384 | Qwen3.8 27B |
| LMArena Text: French | 1399 | 1465 | Qwen3.8 27B |
| LMArena Text: Spanish | 1387 | 1449 | Qwen3.8 27B |
| LMArena Text: Russian | 1402 | 1415 | Qwen3.8 27B |
| LMArena Text: Polish | 1372 | 1439 | Qwen3.8 27B |
| LMArena Text: multi-turn | 1414 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1416 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1389 | 1405 | Qwen3.8 27B |
| LMArena Text: entertainment and media | 1373 | 1393 | Qwen3.8 27B |
| LMArena Vision: English | 1229 | 1267 | Qwen3.8 27B |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | 1258 | 1281 | Qwen3.8 27B |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | 1219 | 1237 | Qwen3.8 27B |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Sol | Qwen3.8 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
Qwen3.8 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | August 14, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $2.00 | $0.04 |
| Output $/Mtok | $10.00 | $1.35 |
| Coding index | 82.8 | 73.6 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Sol better than Qwen3.8 27B for coding?
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 73.6 for Qwen3.8 27B. Pick Qwen3.8 27B when output tokens cost 7.4x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-6 Sol or Qwen3.8 27B?
GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, GPT-6 Sol or Qwen3.8 27B?
GPT-6 Sol, with 1.05M tokens against 1M.
How many benchmarks is the GPT-6 Sol vs Qwen3.8 27B comparison based on?
70 benchmarks have a result for both models as of October 21, 2026. GPT-6 Sol's coding index rests on 2 independent benchmarks and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.