GPT-5.6 Luna vs Qwen3.5-Flash
GPT-5.6 Luna leads Qwen3.5-Flash on coding, 58.0 to 41.1 on the Vibe Coding Index, and Qwen3.5-Flash costs 4.6x less per output token ($0.26 vs $1.20 per million). Updated October 23, 2026; 52 benchmarks have a result for both.
GPT-5.6 Luna or Qwen3.5-Flash: which to pick
Pick GPT-5.6 Luna for coding: it scores 58.0 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash.
Pick Qwen3.5-Flash for output tokens 4.6x cheaper ($0.26 vs $1.20 per million) and work measured by LegalBench (Vals AI) or MortgageTax (Vals AI), where it leads.
- Shared results
- 52
- GPT-5.6 Luna only
- 68
- Qwen3.5-Flash only
- 3
- 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-5.6 Luna
GPT-5.6 Luna scores 58.0 against 41.1.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.6 Luna has a AA Intelligence Index result, 37.3.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Luna has a AA Agentic Index result, 42.1.
Directional only
Reasoning
GPQA Diamond
GPT-5.6 Luna
GPT-5.6 Luna leads, 91.7% 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-5.6 Luna
GPT-5.6 Luna leads, 86.0% against 84.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.6 Luna has a Design Arena: websites result, 1255.
Directional only
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-5.6 Luna $0.0008, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GPT-5.6 Luna $0.04, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Coding, 30.9 points apart on average over 7 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: GPT-5.6 Luna 82.1% against Qwen3.5-Flash 18.2%, 63.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against Qwen3.5-Flash $0.26 per million output tokens, 4.6x apart.
- Context window
- GPT-5.6 Luna 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-5.6 Luna | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 8 | 3 | ||
| General | 21 | 0 | ||
| All shared boards | 49 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1519 | 1244 | GPT-5.6 Luna |
| AA Coding Index | 71.4 | Not measured | Only GPT-5.6 Luna measured |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | 93.0% | 64.4% | GPT-5.6 Luna |
| Terminal-Bench 4 (Vals AI) | 11.6% | Not measured | Only GPT-5.6 Luna measured |
| Vibe Code Bench (Vals AI) | 77.1% | Not measured | Only GPT-5.6 Luna measured |
| Code migration (Vals AI) | 44.5% | Not measured | Only GPT-5.6 Luna measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| IOI (Vals AI) | 61.8% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 79.0% | Not measured | Only GPT-5.6 Luna measured |
| Vibe Code Bench 1-100 (Vals AI) | 22.6% | Not measured | Only GPT-5.6 Luna measured |
| SkillsBench (Vals AI) | 60.5% | Not measured | Only GPT-5.6 Luna measured |
| CyberBench (Vals AI) | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| SciCode | 53.6% | Not measured | Only GPT-5.6 Luna measured |
| FrontierCode | 39.8% | Not measured | Only GPT-5.6 Luna measured |
| WeirdML | 60.9% | Not measured | Only GPT-5.6 Luna measured |
| ALE-Bench | 1667.4 | 265.9 | GPT-5.6 Luna |
| LMArena Text: coding | 1467 | 1412 | GPT-5.6 Luna |
| LMArena Text: software and IT | 1464 | 1416 | GPT-5.6 Luna |
| LMArena WebDev: React | 1508 | 1229 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1520 | 1291 | GPT-5.6 Luna |
| LMArena WebDev: from an image | 1517 | Not measured | Only GPT-5.6 Luna measured |
| SciCode | 6.2% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | Not measured | Only GPT-5.6 Luna measured |
| Epoch Capabilities Index | 156.5 | 143.5 | GPT-5.6 Luna |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench research | 51.0% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench browser | 68.0% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench long agentic | 30.4% | Not measured | Only GPT-5.6 Luna measured |
| SREBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | 55.0% | Not measured | Only GPT-5.6 Luna measured |
| Tax Agent Bench (Vals AI) | 24.8% | Not measured | Only GPT-5.6 Luna measured |
| Legal Research Bench (Vals AI) | 36.5% | Not measured | Only GPT-5.6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | 67.1% | Not measured | Only GPT-5.6 Luna measured |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| Vals Science Index | 25.3% | Not measured | Only GPT-5.6 Luna measured |
| APEX-Agents | 43.0% | Not measured | Only GPT-5.6 Luna measured |
| BALROG | 45.6% | Not measured | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $4,095 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 82.8% | GPT-5.6 Luna |
| LiveBench reasoning | 85.6% | Not measured | Only GPT-5.6 Luna measured |
| MysteryMechanism (Vals AI) | 14.4% | Not measured | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | 85.0% | 81.9% | GPT-5.6 Luna |
| GPQA Diamond (Artificial Analysis run) | 91.1% | Not measured | Only GPT-5.6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | Not measured | Only GPT-5.6 Luna measured |
| GPQA Diamond (Epoch AI run) | 91.6% | 82.3% | GPT-5.6 Luna |
| ARC-AGI | 88.0% | Not measured | Only GPT-5.6 Luna measured |
| ARC-AGI-2 | 59.5% | Not measured | Only GPT-5.6 Luna measured |
| SimpleBench | 46.8% | Not measured | Only GPT-5.6 Luna measured |
| Chess puzzles | 43.0% | 14.0% | GPT-5.6 Luna |
| Mystery game puzzles | 21.0% | 20.0% | GPT-5.6 Luna |
| LMArena Text: hard prompts | 1452 | 1403 | GPT-5.6 Luna |
| LMArena Text: expert | 1478 | 1407 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1460 | 1409 | GPT-5.6 Luna |
| LMArena Vision: diagrams | 1273 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: homework | 1282 | Not measured | Only GPT-5.6 Luna measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | 98.3% | 84.4% | GPT-5.6 Luna |
| FrontierMath, tiers 1 to 3 | 82.1% | 18.2% | GPT-5.6 Luna |
| FrontierMath, tier 4 | 61.0% | Not measured | Only GPT-5.6 Luna measured |
| ProofBench | 60.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: math | 1459 | 1407 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1470 | 1413 | GPT-5.6 Luna |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index83
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 84.1% | GPT-5.6 Luna |
| LegalBench (Vals AI) | 84.0% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | 64.2% | 63.6% | GPT-5.6 Luna |
| TaxEval (Vals AI) | 76.2% | 72.2% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | 67.3% | 67.4% | Qwen3.5-Flash |
| MedCode (Vals AI) | 42.4% | 33.0% | GPT-5.6 Luna |
| SimpleQA Verified | 41.0% | 20.3% | GPT-5.6 Luna |
| LMArena Text: science | 1433 | 1415 | GPT-5.6 Luna |
| LMArena Text: medicine and healthcare | 1398 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1446 | 1397 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1439 | 1394 | GPT-5.6 Luna |
| FACTS Benchmark Suite | 35.0% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: UI components | 1248 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: game dev | 1194 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: data viz | 1212 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: 3D | 1122 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: all code categories | 1230 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: SVG | 1193 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1142 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench design | 76.0% | Not measured | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GPT-5.6 Luna | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 78.0% | Not measured | Only GPT-5.6 Luna measured |
| Public Benefits Bench (Vals AI) | 61.2% | Not measured | Only GPT-5.6 Luna measured |
| SAGE (Vals AI) | 44.2% | 42.5% | GPT-5.6 Luna |
| MedScribe (Vals AI) | 84.4% | 70.6% | GPT-5.6 Luna |
| Vals Multimodal Index | 69.1% | Not measured | Only GPT-5.6 Luna measured |
| Long context reasoning (AA-LCR) | 83.7% | Not measured | Only GPT-5.6 Luna measured |
| DTBench | 89.1% | 82.9% | GPT-5.6 Luna |
| LMCA | 48.5% | 29.1% | GPT-5.6 Luna |
| Surface Evolver Bench | 61.9% | Not measured | Only GPT-5.6 Luna measured |
| Furniture assembly | 42.5% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text | 1431 | 1397 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1438 | 1375 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1396 | 1343 | GPT-5.6 Luna |
| LMArena Vision | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1441 | 1404 | GPT-5.6 Luna |
| LMArena Text: non-English | 1417 | 1385 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1470 | 1446 | GPT-5.6 Luna |
| LMArena Text: German | 1455 | 1391 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1416 | 1369 | GPT-5.6 Luna |
| LMArena Text: Korean | 1415 | 1344 | GPT-5.6 Luna |
| LMArena Text: French | 1457 | 1413 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1445 | 1399 | GPT-5.6 Luna |
| LMArena Text: Russian | 1428 | 1380 | GPT-5.6 Luna |
| LMArena Text: Polish | 1404 | 1376 | GPT-5.6 Luna |
| LMArena Text: multi-turn | 1435 | 1393 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1436 | 1392 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1419 | 1369 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1402 | 1347 | GPT-5.6 Luna |
| LMArena Vision: English | 1263 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: Chinese | 1294 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: reading text | 1264 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: humor | 1190 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: creative writing | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Document | 1457 | Not measured | Only GPT-5.6 Luna measured |
| EQ-Bench 4 | 1156 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Luna | 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-5.6 Luna | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.20 | $0.065 |
| Output $/Mtok | $1.20 | $0.26 |
| Coding index | 58.0 | 41.1 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $0.04 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Qwen3.5-Flash for coding?
Pick GPT-5.6 Luna for coding: it scores 58.0 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash. Pick Qwen3.5-Flash for output tokens 4.6x cheaper ($0.26 vs $1.20 per million) and work measured by LegalBench (Vals AI) or MortgageTax (Vals AI), where it leads.
Which is cheaper, GPT-5.6 Luna or Qwen3.5-Flash?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-5.6 Luna costs $0.04 and Qwen3.5-Flash $0.40.
Which has the bigger context window, GPT-5.6 Luna or Qwen3.5-Flash?
GPT-5.6 Luna, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Luna vs Qwen3.5-Flash comparison based on?
52 benchmarks have a result for both models as of October 23, 2026. GPT-5.6 Luna's coding index rests on 6 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.