GPT-5.6 Luna vs Qwen3.5-122B-A10B
Updated October 21, 2026. 48 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-5.6 Luna for coding: it scores 61.5 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B.
Pick Qwen3.5-122B-A10B when what matters is LMArena Text: medicine and healthcare or LMArena Vision: Chinese, where it leads.
- Shared results
- 48
- GPT-5.6 Luna only
- 68
- Qwen3.5-122B-A10B only
- 3
- Like-for-like categories
- 6 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-5.6 Luna
GPT-5.6 Luna scores 61.5 against 22.9.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 17.7.
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, 1256.
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
GPT-5.6 Luna
GPT-5.6 Luna $0.0008, Qwen3.5-122B-A10B $0.0013 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 61.5 from 2 benchmarks, Qwen3.5-122B-A10B 22.9 from 1 benchmark.
- Widest category
- Intelligence, 19.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: GPT-5.6 Luna 71.4 against Qwen3.5-122B-A10B 43.3, 28.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.7x apart.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, Qwen3.5-122B-A10B 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 | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 3 | 1 | ||
| General | 24 | 2 | ||
| All shared boards | 45 | 3 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1518 | 1361 | GPT-5.6 Luna |
| AA Coding Index | 71.4 | 43.3 | GPT-5.6 Luna |
| 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% | 39.7% | GPT-5.6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Qwen3.5-122B-A10B 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 | 1436 | GPT-5.6 Luna |
| LMArena Text: software and IT | 1463 | 1436 | GPT-5.6 Luna |
| LMArena WebDev: React | 1508 | 1353 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1520 | 1351 | GPT-5.6 Luna |
| LMArena WebDev: from an image | 1517 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | 17.7 | 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 | Qwen3.5-122B-A10B | 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 | 93.6% | Only Qwen3.5-122B-A10B 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 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | 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% | 85.7% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | 25.2% | 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% | 17.0% | GPT-5.6 Luna |
| LMArena Text: hard prompts | 1451 | 1421 | GPT-5.6 Luna |
| LMArena Text: expert | 1478 | 1431 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1460 | 1431 | GPT-5.6 Luna |
| LMArena Vision: diagrams | 1273 | 1249 | GPT-5.6 Luna |
| LMArena Vision: homework | 1282 | 1261 | GPT-5.6 Luna |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | 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 | 1431 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1470 | 1431 | 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 index31
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | 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 | 1432 | GPT-5.6 Luna |
| LMArena Text: medicine and healthcare | 1399 | 1423 | Qwen3.5-122B-A10B |
| LMArena Text: legal and government | 1446 | 1422 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1439 | 1414 | GPT-5.6 Luna |
| 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-5.6 Luna | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: UI components | 1250 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: game dev | 1196 | 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 | 1231 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: SVG | 1193 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1144 | 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 index242
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | 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 | 75.7% | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | 83.7% | 76.3% | GPT-5.6 Luna |
| DTBench | 89.1% | 84.3% | GPT-5.6 Luna |
| LMCA | 48.5% | 32.2% | 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 | 1417 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1437 | 1399 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1396 | 1368 | GPT-5.6 Luna |
| LMArena Vision | 1257 | 1244 | GPT-5.6 Luna |
| LMArena Text: English | 1441 | 1428 | GPT-5.6 Luna |
| LMArena Text: non-English | 1417 | 1401 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1470 | 1463 | GPT-5.6 Luna |
| LMArena Text: German | 1455 | 1425 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1415 | 1367 | GPT-5.6 Luna |
| LMArena Text: Korean | 1415 | 1353 | GPT-5.6 Luna |
| LMArena Text: French | 1458 | 1443 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1446 | 1425 | GPT-5.6 Luna |
| LMArena Text: Russian | 1427 | 1400 | GPT-5.6 Luna |
| LMArena Text: Polish | 1405 | 1398 | GPT-5.6 Luna |
| LMArena Text: multi-turn | 1434 | 1416 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1436 | 1410 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1419 | 1387 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1402 | 1366 | GPT-5.6 Luna |
| LMArena Vision: English | 1263 | 1249 | GPT-5.6 Luna |
| LMArena Vision: Chinese | 1294 | 1301 | Qwen3.5-122B-A10B |
| LMArena Vision: reading text | 1264 | 1251 | GPT-5.6 Luna |
| LMArena Vision: humor | 1190 | 1243 | Qwen3.5-122B-A10B |
| LMArena Vision: creative writing | 1257 | 1220 | GPT-5.6 Luna |
| LMArena Document | 1457 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Luna | Qwen3.5-122B-A10B | 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 costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Luna costs less
Agent loop
200K input + 10K output tokens
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 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | February 25, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.20 | $0.26 |
| Output $/Mtok | $1.20 | $2.08 |
| Coding index | 61.5 | 22.9 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Qwen3.5-122B-A10B for coding?
Pick GPT-5.6 Luna for coding: it scores 61.5 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B. Pick Qwen3.5-122B-A10B when what matters is LMArena Text: medicine and healthcare or LMArena Vision: Chinese, where it leads.
Which is cheaper, GPT-5.6 Luna or Qwen3.5-122B-A10B?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or Qwen3.5-122B-A10B?
GPT-5.6 Luna, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Luna vs Qwen3.5-122B-A10B comparison based on?
48 benchmarks have a result for both models as of October 21, 2026. GPT-5.6 Luna's coding index rests on 2 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.