GPT-5.6 Luna vs Qwen3 VL 8B Instruct
GPT-5.6 Luna leads Qwen3 VL 8B Instruct on the Artificial Analysis Intelligence Index, 37.3 to 7.3, and Qwen3 VL 8B Instruct costs 1.6x less per output token ($0.75 vs $1.20 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
vs boards won of 4 shared
GPT-5.6 Luna or Qwen3 VL 8B Instruct: which to pick
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Qwen3 VL 8B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen3 VL 8B Instruct for output tokens 1.6x cheaper ($0.75 vs $1.20 per million).
Model AGPT-5.6 LunaOpenAI58.0/1006 independent benchmarks$0.04 per solved taskModel BQwen3 VL 8B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 4
- GPT-5.6 Luna only
- 116
- Qwen3 VL 8B Instruct only
- 7
- Like-for-like categories
- 3 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
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 7.3.
Same board, both measured
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 8B Instruct
GPT-5.6 Luna $0.0008, Qwen3 VL 8B Instruct $0.0006 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 VL 8B Instruct not benchmarked.
- Widest category
- General, 50.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.6 Luna 83.7% against Qwen3 VL 8B Instruct 33.3%, 50.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against Qwen3 VL 8B Instruct $0.75 per million output tokens, 1.6x apart.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, Qwen3 VL 8B Instruct 262K.
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 VL 8B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index25 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 35.3% | Only Qwen3 VL 8B Instruct measured |
| SciCode | 53.6% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Qwen3 VL 8B Instruct 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 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | 7.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | 156.5 | Not measured | Only GPT-5.6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | 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 |
| τ²-Bench Telecom | Not measured | 29.2% | Only Qwen3 VL 8B Instruct 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 index20
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.6 Luna measured |
| 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% | Not measured | Only GPT-5.6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | 91.1% | 57.9% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | 3.8% | GPT-5.6 Luna |
| 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 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 30.7 | Only Qwen3 VL 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 30.7% | Only Qwen3 VL 8B Instruct measured |
| 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 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | 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 |
| MMLU Pro (Artificial Analysis run) | Not measured | 74.9% | Only Qwen3 VL 8B Instruct 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 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | 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 | 1195 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: data viz | 1212 | 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 | 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 index10
| Benchmark | GPT-5.6 Luna | Qwen3 VL 8B Instruct | 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% | 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 |
| IFBench | Not measured | 39.9% | Only Qwen3 VL 8B Instruct measured |
| Long context reasoning (AA-LCR) | 83.7% | 33.3% | GPT-5.6 Luna |
| 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 |
| EQ-Bench 4 | 1156 | 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 VL 8B Instruct | 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
GPT-5.6 Luna$0.0008Fits in one request
Qwen3 VL 8B Instruct$0.0006Fits in one request
Qwen3 VL 8B Instruct costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Luna$0.014Fits in one request
Qwen3 VL 8B Instruct$0.015Fits in one request
GPT-5.6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Luna$0.052Fits in one request
Qwen3 VL 8B Instruct$0.058Fits in one request
GPT-5.6 Luna 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 VL 8B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | October 14, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.20 | $0.25 |
| Output $/Mtok | $1.20 | $0.75 |
| Coding index | 58.0 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Qwen3 VL 8B Instruct for coding?
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Qwen3 VL 8B Instruct has none, so the two cannot be ranked on coding. Pick Qwen3 VL 8B Instruct for output tokens 1.6x cheaper ($0.75 vs $1.20 per million).
Which is cheaper, GPT-5.6 Luna or Qwen3 VL 8B Instruct?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3 VL 8B Instruct costs $0.25 and $0.75. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or Qwen3 VL 8B Instruct?
GPT-5.6 Luna, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Luna vs Qwen3 VL 8B Instruct comparison based on?
4 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 VL 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.