GLM 4.7 vs Muse Ember
Muse Ember leads GLM 4.7 on coding, 64.9 to 55.0 on the Vibe Coding Index, and Muse Ember costs 4.4x less per output token ($0.50 vs $2.20 per million). Updated November 14, 2026; 3 benchmarks have a result for both.
GLM 4.7 or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 55.0 for GLM 4.7.
GLM 4.7 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 3
- GLM 4.7 only
- 79
- Muse Ember only
- 12
- Like-for-like categories
- 1 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 55.0.
2 and 3 benchmarks behind the scores
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 has a Design Arena: websites result, 1217.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GLM 4.7 $0.0017, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.0 from 2 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 27.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 4.7 1415 against Muse Ember 1616, 30.9 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Muse Ember $0.50 per million output tokens, 4.4x apart.
- Context window
- GLM 4.7 takes 205K 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 | GLM 4.7 | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| All shared boards | 0 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1609 | Muse Ember |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 39.4% | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | Not measured | 83.7% | Only Muse Ember measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | Not measured | 40.9% | Only Muse Ember measured |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: React | 1415 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1446 | 1621 | Muse Ember |
| Terminal-Bench 2.0 | 33.4% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 143.3 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1024 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1097 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1097 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| Tax Agent Bench (Vals AI) | Not measured | 38.2% | Only Muse Ember measured |
| Legal Research Bench (Vals AI) | Not measured | 49.0% | Only Muse Ember measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| MCP Atlas | 58.1% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | Not measured | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | Not measured | Only GLM 4.7 measured |
| HLE-Diamond (Artificial Analysis run) | Not measured | 25.1% | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1413 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1470 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1430 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1217 | Not measured | Only GLM 4.7 measured |
| Design Arena: UI components | 1189 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1178 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1192 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1206 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1132 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1160 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | GLM 4.7 | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | Not measured | Only GLM 4.7 measured |
| GDPval-AA | 1021 | Not measured | Only GLM 4.7 measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1402 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1494 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1430 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1401 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | 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 | Z.ai GLM 4.7 | Meta Muse Ember |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | November 9, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.60 | $0.10 |
| Output $/Mtok | $2.20 | $0.50 |
| Coding index | 55.0 | 64.9 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 55.0 for GLM 4.7. GLM 4.7 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GLM 4.7 or Muse Ember?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Muse Ember?
Muse Ember, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Muse Ember comparison based on?
3 benchmarks have a result for both models as of November 14, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.