GLM 5.3 Flash vs Muse Ember
Muse Ember leads GLM 5.3 Flash on coding, 64.9 to 56.2 on the Vibe Coding Index, at about the same output price ($0.50 vs $0.50 per million). Updated November 14, 2026; 15 benchmarks have a result for both.
GLM 5.3 Flash or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.2 for GLM 5.3 Flash, though on 3 benchmarks against 6.
Pick GLM 5.3 Flash for work measured by LMArena WebDev or LMArena WebDev: React, where it leads.
- Shared results
- 15
- GLM 5.3 Flash only
- 100
- Muse Ember only
- 0
- Like-for-like categories
- 5 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 56.2.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GLM 5.3 Flash has a AA Intelligence Index result, 27.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1257.
Directional only
General
Vals Index
Muse Ember
Muse Ember leads, 60.5% against 50.9%.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GLM 5.3 Flash $0.0004, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- GLM 5.3 Flash 56.2 from 6 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 11.0 points apart on average over 7 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.3 Flash 30.8% against Muse Ember 83.7%, 52.9 points apart on a 0 to 100 scale.
- Output price
- Both cost $0.50 per million output tokens.
- Context window
- Both take 1.05M tokens.
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 5.3 Flash | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 2 | 5 | ||
| Agentic work | 0 | 4 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 2 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index25
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| LiveBench coding | 70.9% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1609 | 1609 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 30.8% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 20.5% | Not measured | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| SciCode | 51.6% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 32.8% | 40.9% | Muse Ember |
| FrontierCode 2.0 | 17.6% | 27.2% | Muse Ember |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| ALE-Bench | 303.6 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: coding | 1509 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: software and IT | 1501 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: React | 1628 | 1616 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1576 | 1621 | Muse Ember |
| LMArena WebDev: from an image | 1582 | Not measured | Only GLM 5.3 Flash measured |
| GameBench 2 | 60.8 | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4.0 | 36.7% | Not measured | Only GLM 5.3 Flash measured |
| CursorBench 4.0 | 36.8% | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.8 | Not measured | Only GLM 5.3 Flash measured |
| Epoch Capabilities Index | 153.8 | Not measured | Only GLM 5.3 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 73.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 59.8% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 20.7% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | 31.6% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 45.2% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | 25.0% | Muse Ember |
| Excel Modeling Benchmark (Vals AI) | 55.9% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| APEX-Agents | 52.8% | 64.3% | Muse Ember |
| PostTrainBench | 36.8% | Not measured | Only GLM 5.3 Flash measured |
| Toolathlon-Verified | 78.4% | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | Not measured | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 91.2% | Not measured | Only GLM 5.3 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | Not measured | Only GLM 5.3 Flash measured |
| HLE-Diamond (Artificial Analysis run) | 18.2% | 25.1% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | 24.5% | 39.0% | Muse Ember |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: expert | 1511 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts in English | 1492 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: diagrams | 1321 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | 1323 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1503 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: mathematical work | 1491 | Not measured | Only GLM 5.3 Flash 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 | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | Not measured | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | 83.9% | Not measured | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | 75.6% | Not measured | Only GLM 5.3 Flash measured |
| AA-Omniscience | 0.7 | 7.7 | Muse Ember |
| LMArena Text: science | 1485 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: medicine and healthcare | 1480 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: legal and government | 1478 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: business and finance | 1461 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1257 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: UI components | 1296 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: game dev | 1266 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: data viz | 1258 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: 3D | 1292 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | 1267 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: SVG | 1271 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1269 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 73.7% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.3 Flash | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | 50.9% | 60.5% | Muse Ember |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| SAGE (Vals AI) | 89.3% | Not measured | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | 88.9% | Not measured | Only GLM 5.3 Flash measured |
| Long context reasoning (AA-LCR) | 80.0% | Not measured | Only GLM 5.3 Flash measured |
| GDPval-AA | 1648 | Not measured | Only GLM 5.3 Flash measured |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1472 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: instruction following | 1480 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: creative writing | 1442 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision | 1295 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1477 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: non-English | 1462 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Chinese | 1528 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: German | 1452 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Japanese | 1453 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | 1427 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: French | 1489 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | 1476 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | 1467 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Polish | 1448 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | 1471 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: writing and language | 1457 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: entertainment and media | 1442 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: English | 1299 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | 1359 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | 1303 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | 1291 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: recognizing things | 1279 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | 1272 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | 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 5.3 Flash | Meta Muse Ember |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | No public weights listed |
| Released | August 26, 2026 | November 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.10 |
| Output $/Mtok | $0.50 | $0.50 |
| Coding index | 56.2 | 64.9 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $0.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.2 for GLM 5.3 Flash, though on 3 benchmarks against 6. Pick GLM 5.3 Flash for work measured by LMArena WebDev or LMArena WebDev: React, where it leads.
Which is cheaper, GLM 5.3 Flash or Muse Ember?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, GLM 5.3 Flash or Muse Ember?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.3 Flash vs Muse Ember comparison based on?
15 benchmarks have a result for both models as of November 14, 2026. GLM 5.3 Flash'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.