GPT-5.6 Terra vs Muse Ember
Muse Ember leads GPT-5.6 Terra on coding, 64.9 to 63.5 on the Vibe Coding Index, and Muse Ember costs 24.0x less per output token ($0.50 vs $12.00 per million). Updated November 14, 2026; 13 benchmarks have a result for both.
GPT-5.6 Terra or Muse Ember: which to pick
GPT-5.6 Terra and Muse Ember are level for coding, 63.5 against 64.9 on the Vibe Coding Index, so price and context decide.
Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by HLE-Diamond (Artificial Analysis run) or CritPt v2, hard subset (Artificial Analysis run), where it leads. Pick Muse Ember for output tokens 24.0x cheaper ($0.50 vs $12.00 per million) and work measured by LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.
- Shared results
- 13
- GPT-5.6 Terra only
- 129
- 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
No call
Level: 63.5 against 64.9, inside 2 points.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.6 Terra has a AA Intelligence Index result, 32.3.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Terra has a GPQA Diamond result, 90.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Terra has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.6 Terra has a Design Arena: websites result, 1260.
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 Terra
GPT-5.6 Terra takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.6 Terra $0.0080, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only
At a glance
- Coding index
- GPT-5.6 Terra 63.5 from 6 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 11.5 points apart on average over 7 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): GPT-5.6 Terra 22.7% against Muse Ember 39.4%, 22.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Terra $12.00 against Muse Ember $0.50 per million output tokens, 24.0x apart.
- Context window
- GPT-5.6 Terra 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 Terra | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 2 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| LiveBench coding | 84.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1520 | 1609 | Muse Ember |
| AA Coding Index | 76.7 | Not measured | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | 85.9% | Not measured | Only GPT-5.6 Terra measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 4 (Vals AI) | 22.7% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 74.6% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 47.8% | Not measured | Only GPT-5.6 Terra measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | 87.6% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | Not measured | Only GPT-5.6 Terra measured |
| Vibe Code Bench 1-100 (Vals AI) | 14.8% | Not measured | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | 58.9% | Not measured | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 55.0% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | 62.9% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 35.4% | 40.9% | Muse Ember |
| FrontierCode 2.0 | 23.9% | 27.2% | Muse Ember |
| FrontierCode | 41.3% | Not measured | Only GPT-5.6 Terra measured |
| WeirdML | 78.3% | Not measured | Only GPT-5.6 Terra measured |
| MirrorCode | 41.1% | Not measured | Only GPT-5.6 Terra measured |
| ALE-Bench | 1951.4 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: coding | 1485 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: software and IT | 1481 | Not measured | Only GPT-5.6 Terra measured |
| LMArena WebDev: React | 1511 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1523 | 1621 | Muse Ember |
| LMArena WebDev: from an image | 1539 | Not measured | Only GPT-5.6 Terra measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Terra measured |
| SWE-Bench Pro V2 | 92.4% | Not measured | Only GPT-5.6 Terra measured |
| GameBench 2 | 53.5 | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 | 78.4% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 3.0 | 20.8% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench 4.0 | 21.5% | Not measured | Only GPT-5.6 Terra measured |
| CursorBench 4.0 | 41.3% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.3 | Not measured | Only GPT-5.6 Terra measured |
| Epoch Capabilities Index | 160.7 | Not measured | Only GPT-5.6 Terra measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 43.2 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building web apps | 1095 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building full-stack apps | 1036 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building mobile apps | 1097 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | 1057 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | 1027 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | 999 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | 1095 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: spreadsheets | 999 | Not measured | Only GPT-5.6 Terra measured |
| LiveBench research | 79.7% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench browser | 70.3% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench long agentic | 25.9% | Not measured | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | 9.5% | Not measured | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | 54.4% | Not measured | Only GPT-5.6 Terra measured |
| Tax Agent Bench (Vals AI) | 30.0% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 41.4% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| Excel Modeling Benchmark (Vals AI) | 66.2% | Not measured | Only GPT-5.6 Terra measured |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | 86.3% | Not measured | Only GPT-5.6 Terra measured |
| APEX-Agents | 58.2% | 64.3% | Muse Ember |
| BALROG | 53.2% | Not measured | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | $7,343 | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 90.9% | Not measured | Only GPT-5.6 Terra measured |
| LiveBench reasoning | 90.6% | Not measured | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | 86.5% | Not measured | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | 92.5% | Not measured | Only GPT-5.6 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.9% | Not measured | Only GPT-5.6 Terra measured |
| HLE-Diamond (Artificial Analysis run) | 28.8% | 25.1% | GPT-5.6 Terra |
| CritPt v2, hard subset (Artificial Analysis run) | 50.5% | 39.0% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 93.3% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI | 96.5% | Not measured | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | 83.9% | Not measured | Only GPT-5.6 Terra measured |
| SimpleBench | 48.9% | Not measured | Only GPT-5.6 Terra measured |
| Chess puzzles | 47.0% | Not measured | Only GPT-5.6 Terra measured |
| Mystery game puzzles | 35.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts | 1470 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: expert | 1495 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts in English | 1465 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: diagrams | 1291 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | 1308 | Not measured | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | 51.3% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.7% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tiers 1 to 3 | 86.0% | Not measured | Only GPT-5.6 Terra measured |
| FrontierMath, tier 4 | 70.7% | Not measured | Only GPT-5.6 Terra measured |
| ProofBench | 74.0% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: math | 1462 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: mathematical work | 1476 | Not measured | Only GPT-5.6 Terra 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 Terra | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GPT-5.6 Terra measured |
| LegalBench (Vals AI) | 85.1% | Not measured | Only GPT-5.6 Terra measured |
| CorpFin (Vals AI) | 65.3% | Not measured | Only GPT-5.6 Terra measured |
| TaxEval (Vals AI) | 76.2% | Not measured | Only GPT-5.6 Terra measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | 43.4% | Not measured | Only GPT-5.6 Terra measured |
| AA-Omniscience | -9.3 | 7.7 | Muse Ember |
| SimpleQA Verified | 43.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: science | 1453 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: medicine and healthcare | 1430 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: legal and government | 1461 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: business and finance | 1446 | Not measured | Only GPT-5.6 Terra measured |
| FACTS Benchmark Suite | 61.3% | Not measured | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1260 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: UI components | 1250 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: game dev | 1220 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: data viz | 1236 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: 3D | 1109 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: all code categories | 1229 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: SVG | 1207 | Not measured | Only GPT-5.6 Terra measured |
| Design Arena: ASCII art | 1171 | Not measured | Only GPT-5.6 Terra measured |
| LiveBench design | 70.9% | Not measured | Only GPT-5.6 Terra measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index38 boards, none shared
| Benchmark | GPT-5.6 Terra | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| LiveBench data analysis | 79.3% | Not measured | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | 62.4% | Not measured | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Terra measured |
| MedScribe (Vals AI) | 82.9% | Not measured | Only GPT-5.6 Terra measured |
| Vals Multimodal Index | 65.1% | Not measured | Only GPT-5.6 Terra measured |
| IFBench | 71.2% | Not measured | Only GPT-5.6 Terra measured |
| Long context reasoning (AA-LCR) | 83.0% | Not measured | Only GPT-5.6 Terra measured |
| GDPval-AA | 1461 | Not measured | Only GPT-5.6 Terra measured |
| DTBench | 93.3% | Not measured | Only GPT-5.6 Terra measured |
| LMCA | 54.9% | Not measured | Only GPT-5.6 Terra measured |
| Surface Evolver Bench | 83.8% | Not measured | Only GPT-5.6 Terra measured |
| Furniture assembly | 54.2% | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text | 1449 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: instruction following | 1455 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: creative writing | 1414 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision | 1269 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1448 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: non-English | 1442 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Chinese | 1513 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: German | 1461 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Japanese | 1445 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Korean | 1423 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: French | 1470 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Spanish | 1449 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Russian | 1453 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: Polish | 1443 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: multi-turn | 1455 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: longer queries | 1454 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: writing and language | 1431 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Text: entertainment and media | 1418 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: English | 1268 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | 1326 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | 1277 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | 1241 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | 1245 | Not measured | Only GPT-5.6 Terra measured |
| LMArena Document | 1468 | Not measured | Only GPT-5.6 Terra measured |
| EQ-Bench 4 | 1220 | Not measured | Only GPT-5.6 Terra 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 Terra | 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 Terra | 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 | $2.00 | $0.10 |
| Output $/Mtok | $12.00 | $0.50 |
| Coding index | 63.5 | 64.9 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $0.42 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Terra better than Muse Ember for coding?
GPT-5.6 Terra and Muse Ember are level for coding, 63.5 against 64.9 on the Vibe Coding Index, so price and context decide. Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by HLE-Diamond (Artificial Analysis run) or CritPt v2, hard subset (Artificial Analysis run), where it leads. Pick Muse Ember for output tokens 24.0x cheaper ($0.50 vs $12.00 per million) and work measured by LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.
Which is cheaper, GPT-5.6 Terra or Muse Ember?
GPT-5.6 Terra costs $2.00 per million input tokens and $12.00 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GPT-5.6 Terra only.
Which has the bigger context window, GPT-5.6 Terra or Muse Ember?
GPT-5.6 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Terra vs Muse Ember comparison based on?
13 benchmarks have a result for both models as of November 14, 2026. GPT-5.6 Terra's coding index rests on 6 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.