GLM 5.3 vs Muse Spark Open
Updated October 21, 2026. 26 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 62.1 for Muse Spark Open.
Pick Muse Spark Open when what matters is ALE-Bench or LiveBench research, where it leads.
- Shared results
- 26
- GLM 5.3 only
- 74
- Muse Spark Open only
- 0
- 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
GLM 5.3
GLM 5.3 scores 73.1 against 62.1.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GLM 5.3 has a AA Intelligence Index result, 44.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
Muse Spark Open
Muse Spark Open leads, 1311 against 1295.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0008, Muse Spark Open $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, Muse Spark Open 62.1 from 2 benchmarks.
- Widest category
- Agentic work, 14.4 points apart on average over 8 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 0.0% against Muse Spark Open 36.0%, 36.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against Muse Spark Open $4.25 per million output tokens, 3.0x apart.
- 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 | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Coding | 8 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 5 | 3 | ||
| Reasoning | 2 | 1 | ||
| Web design | 2 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 20 | 6 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index81
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | 73.6% | GLM 5.3 |
| LMArena WebDev | 1621 | 1528 | GLM 5.3 |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | 6.6% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 76.2% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 21.2% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | 58.4% | GLM 5.3 |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | 1590.1 | Muse Spark Open |
| LMArena Text: coding | 1497 | Not measured | Only GLM 5.3 measured |
| LMArena Text: software and IT | 1493 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: React | 1641 | 1529 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 1495 | GLM 5.3 |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | Not measured | Only GLM 5.3 measured |
| Epoch Capabilities Index | 156.0 | 155.1 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1255 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | 80.3% | Muse Spark Open |
| LiveBench browser | 0.0% | 36.0% | Muse Spark Open |
| LiveBench long agentic | 35.9% | 18.9% | GLM 5.3 |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | 61.3% | Muse Spark Open |
| Tax Agent Bench (Vals AI) | 39.7% | 26.3% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 38.5% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 53.7% | GLM 5.3 |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| APEX-Agents | 56.6% | 33.4% | GLM 5.3 |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | 73.7% | GLM 5.3 |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 89.0% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 43.9% | Muse Spark Open |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 measured |
| LMArena Text: expert | 1514 | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts in English | 1496 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | Not measured | Only GLM 5.3 measured |
| LMArena Text: mathematical work | 1497 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | Not measured | Only GLM 5.3 measured |
| LMArena Text: medicine and healthcare | 1481 | Not measured | Only GLM 5.3 measured |
| LMArena Text: legal and government | 1473 | Not measured | Only GLM 5.3 measured |
| LMArena Text: business and finance | 1459 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1311 | Muse Spark Open |
| Design Arena: UI components | 1314 | Not measured | Only GLM 5.3 measured |
| Design Arena: game dev | 1311 | 1290 | GLM 5.3 |
| Design Arena: data viz | 1260 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | Not measured | Only GLM 5.3 measured |
| Design Arena: SVG | 1292 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | 60.3% | GLM 5.3 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Muse Spark Open | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | 60.2% | GLM 5.3 |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| Long context reasoning (AA-LCR) | 79.7% | 78.3% | GLM 5.3 |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | Not measured | Only GLM 5.3 measured |
| LMArena Text: instruction following | 1479 | Not measured | Only GLM 5.3 measured |
| LMArena Text: creative writing | 1458 | Not measured | Only GLM 5.3 measured |
| LMArena Text: English | 1482 | Not measured | Only GLM 5.3 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Chinese | 1532 | Not measured | Only GLM 5.3 measured |
| LMArena Text: German | 1488 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1503 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Spanish | 1462 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Russian | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Polish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1472 | Not measured | Only GLM 5.3 measured |
| LMArena Text: longer queries | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: writing and language | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: entertainment and media | 1447 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | Muse Spark Open | 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
GLM 5.3 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 | Meta Muse Spark Open |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.079 | $1.25 |
| Output $/Mtok | $1.40 | $4.25 |
| Coding index | 73.1 | 62.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Muse Spark Open for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 62.1 for Muse Spark Open. Pick Muse Spark Open when what matters is ALE-Bench or LiveBench research, where it leads.
Which is cheaper, GLM 5.3 or Muse Spark Open?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or Muse Spark Open?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.3 vs Muse Spark Open comparison based on?
26 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Muse Spark Open's on 2. Where only one model has a result, the page says so instead of naming a winner.