GPT-5.6 Luna vs Qwen3.8 Flash
Qwen3.8 Flash costs 2.6x less per output token than GPT-5.6 Luna ($0.47 vs $1.20 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GPT-5.6 Luna or Qwen3.8 Flash: which to pick
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Qwen3.8 Flash has none, so the two cannot be ranked on coding.
Pick Qwen3.8 Flash for output tokens 2.6x cheaper ($0.47 vs $1.20 per million) and work measured by LiveBench coding or LiveBench research, where it leads.
- Shared results
- 7
- GPT-5.6 Luna only
- 112
- Qwen3.8 Flash only
- 0
- Like-for-like categories
- 5 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 GPT-5.6 Luna has a coding score, so no call is made.
One side not measured
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
No call
Only GPT-5.6 Luna has a GPQA Diamond result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Luna has a MMLU Pro result, 86.0%.
Directional only
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
Qwen3.8 Flash
GPT-5.6 Luna $0.0008, Qwen3.8 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Luna has the number, $0.04.
Directional only
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, Qwen3.8 Flash not benchmarked.
- Widest category
- Agentic work, 16.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench research: GPT-5.6 Luna 51.0% against Qwen3.8 Flash 74.6%, 23.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against Qwen3.8 Flash $0.47 per million output tokens, 2.6x apart.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, Qwen3.8 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 | Qwen3.8 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 1 | 2 | ||
| Reasoning | 0 | 1 | ||
| Web design | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 3 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | 87.4% | Qwen3.8 Flash |
| LMArena WebDev | 1518 | Not measured | Only GPT-5.6 Luna measured |
| AA Coding Index | 71.4 | Not measured | Only GPT-5.6 Luna measured |
| SWE-bench Verified (Vals AI) | 93.0% | Not measured | Only GPT-5.6 Luna measured |
| 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 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1466 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: software and IT | 1463 | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev: React | 1508 | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev: HTML | 1520 | Not measured | Only GPT-5.6 Luna measured |
| 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 16 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | Not measured | Only GPT-5.6 Luna measured |
| Epoch Capabilities Index | 156.5 | Not measured | Only GPT-5.6 Luna measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench research | 51.0% | 74.6% | Qwen3.8 Flash |
| LiveBench browser | 68.0% | 48.0% | GPT-5.6 Luna |
| LiveBench long agentic | 30.4% | 37.1% | Qwen3.8 Flash |
| 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 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench reasoning | 85.6% | 87.4% | Qwen3.8 Flash |
| MysteryMechanism (Vals AI) | 14.4% | Not measured | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | 85.0% | Not measured | Only GPT-5.6 Luna measured |
| 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% | Not measured | Only GPT-5.6 Luna measured |
| 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% | Not measured | Only GPT-5.6 Luna measured |
| Mystery game puzzles | 21.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts | 1451 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: expert | 1478 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts in English | 1460 | Not measured | Only GPT-5.6 Luna measured |
| 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 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only GPT-5.6 Luna measured |
| FrontierMath, tiers 1 to 3 | 82.1% | Not measured | Only GPT-5.6 Luna measured |
| 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 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: mathematical work | 1470 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GPT-5.6 Luna measured |
| LegalBench (Vals AI) | 84.0% | Not measured | Only GPT-5.6 Luna measured |
| CorpFin (Vals AI) | 64.2% | Not measured | Only GPT-5.6 Luna measured |
| TaxEval (Vals AI) | 76.2% | Not measured | Only GPT-5.6 Luna measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Luna measured |
| MedCode (Vals AI) | 42.4% | Not measured | Only GPT-5.6 Luna measured |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1433 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: medicine and healthcare | 1399 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: legal and government | 1446 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: business and finance | 1439 | Not measured | Only GPT-5.6 Luna measured |
| FACTS Benchmark Suite | 35.0% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Luna | Qwen3.8 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 | 1211 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: 3D | 1121 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: all code categories | 1231 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: SVG | 1194 | 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% | 72.8% | GPT-5.6 Luna |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Luna | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 78.0% | 74.2% | GPT-5.6 Luna |
| Public Benefits Bench (Vals AI) | 61.2% | Not measured | Only GPT-5.6 Luna measured |
| SAGE (Vals AI) | 44.2% | Not measured | Only GPT-5.6 Luna measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.6 Luna measured |
| 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% | Not measured | Only GPT-5.6 Luna measured |
| LMCA | 48.5% | Not measured | Only GPT-5.6 Luna measured |
| 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 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: instruction following | 1437 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: creative writing | 1396 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1441 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: non-English | 1417 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Chinese | 1470 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: German | 1455 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Korean | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: French | 1458 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Spanish | 1446 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Russian | 1427 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Polish | 1405 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: multi-turn | 1434 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: longer queries | 1436 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: writing and language | 1419 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: entertainment and media | 1402 | Not measured | Only GPT-5.6 Luna measured |
| 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 |
| 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 | GPT-5.6 Luna | Qwen3.8 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
Qwen3.8 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 | Qwen3.8 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | August 26, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.20 | $0.15 |
| Output $/Mtok | $1.20 | $0.47 |
| Coding index | 58.0 | Not benchmarked |
| Independent measurements behind it | 6 | 1 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Qwen3.8 Flash for coding?
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Qwen3.8 Flash has none, so the two cannot be ranked on coding. Pick Qwen3.8 Flash for output tokens 2.6x cheaper ($0.47 vs $1.20 per million) and work measured by LiveBench coding or LiveBench research, where it leads.
Which is cheaper, GPT-5.6 Luna or Qwen3.8 Flash?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3.8 Flash costs $0.15 and $0.47. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or Qwen3.8 Flash?
GPT-5.6 Luna, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Luna vs Qwen3.8 Flash comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Luna's coding index rests on 6 independent benchmarks and Qwen3.8 Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.