GPT-5.6 Luna vs Muse Ember
Muse Ember leads GPT-5.6 Luna on coding, 64.9 to 55.3 on the Vibe Coding Index, and Muse Ember costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated November 14, 2026; 13 benchmarks have a result for both.
GPT-5.6 Luna or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 55.3 for GPT-5.6 Luna, though on 3 benchmarks against 5.
Pick GPT-5.6 Luna for a larger context window (1.05M against 1.05M tokens).
- Shared results
- 13
- GPT-5.6 Luna only
- 112
- Muse Ember only
- 2
- Like-for-like categories
- 4 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
Muse Ember
Muse Ember scores 64.9 against 55.3.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.6 Luna has a AA Intelligence Index result, 23.8.
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, 1248.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.6 Luna $0.0008, Muse Ember $0.0003 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 55.3 from 5 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 17.7 points apart on average over 7 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): GPT-5.6 Luna 11.6% against Muse Ember 39.4%, 37.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against Muse Ember $0.50 per million output tokens, 2.4x apart.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, Muse Ember 1.05M.
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 | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1519 | 1609 | Muse Ember |
| 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% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 77.1% | 83.7% | Muse Ember |
| 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 |
| Terminal-Bench 4.0 (Artificial Analysis run) | 11.6% | 40.9% | Muse Ember |
| FrontierCode 2.0 | 17.6% | 27.2% | Muse Ember |
| FrontierCode | 39.8% | Not measured | Only GPT-5.6 Luna measured |
| WeirdML | 60.9% | Not measured | Only GPT-5.6 Luna measured |
| MirrorCode | 21.1% | Not measured | Only GPT-5.6 Luna measured |
| ALE-Bench | 1667.4 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1467 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: software and IT | 1464 | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev: React | 1508 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1522 | 1621 | Muse Ember |
| 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 |
| GameBench 2 | 54.0 | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 | 75.7% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 3.0 | 14.3% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 4.0 | 17.3% | Not measured | Only GPT-5.6 Luna measured |
| CursorBench 4.0 | 35.9% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.8 | Not measured | Only GPT-5.6 Luna measured |
| Epoch Capabilities Index | 156.7 | Not measured | Only GPT-5.6 Luna measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | 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% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 36.5% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember 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% | 64.3% | Muse Ember |
| 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 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 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% | 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 |
| HLE-Diamond (Artificial Analysis run) | 20.9% | 25.1% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | 30.5% | 39.0% | Muse Ember |
| 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 | 1452 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: expert | 1477 | 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 | 1271 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: homework | 1281 | Not measured | Only GPT-5.6 Luna measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only GPT-5.6 Luna measured |
| GoBench | 1091 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Luna | Muse Ember | 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 | 1457 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: mathematical work | 1467 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.6 Luna | Muse Ember | 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 |
| AA-Omniscience | -17.1 | 7.7 | Muse Ember |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1434 | 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 | 1444 | 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 | 56.9% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1248 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: UI components | 1241 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: game dev | 1190 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: data viz | 1203 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: 3D | 1099 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: all code categories | 1221 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: SVG | 1185 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1142 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index35 boards, none shared
| Benchmark | GPT-5.6 Luna | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| Public Benefits Bench (Vals AI) | 61.2% | 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 |
| GDPval-AA | 1470 | 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 | 1256 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1442 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: non-English | 1417 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Chinese | 1472 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: German | 1456 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Japanese | 1416 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Korean | 1408 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Spanish | 1448 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Russian | 1426 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Polish | 1403 | 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 | 1418 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: entertainment and media | 1401 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: English | 1260 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: Chinese | 1301 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: reading text | 1263 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: humor | 1192 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: creative writing | 1256 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Document | 1454 | Not measured | Only GPT-5.6 Luna measured |
| EQ-Bench 4 | 1146 | Not measured | Only GPT-5.6 Luna measured |
| 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-5.6 Luna | Muse Ember | 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
Muse Ember costs less
Repository review
50K input + 3K output tokens
Muse Ember costs less
Agent loop
200K input + 10K output tokens
Muse Ember 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 | Meta Muse Ember |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | November 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.20 | $0.10 |
| Output $/Mtok | $1.20 | $0.50 |
| Coding index | 55.3 | 64.9 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 55.3 for GPT-5.6 Luna, though on 3 benchmarks against 5. Pick GPT-5.6 Luna for a larger context window (1.05M against 1.05M tokens).
Which is cheaper, GPT-5.6 Luna or Muse Ember?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or Muse Ember?
GPT-5.6 Luna, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Luna vs Muse Ember comparison based on?
13 benchmarks have a result for both models as of November 14, 2026. GPT-5.6 Luna's coding index rests on 5 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.