GLM 4.5V vs Muse Spark Open
GLM 4.5V costs 2.4x less per output token than Muse Spark Open ($1.80 vs $4.25 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GLM 4.5V or Muse Spark Open: which to pick
Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.
Pick GLM 4.5V for output tokens 2.4x cheaper ($1.80 vs $4.25 per million).
- Shared results
- 3
- GLM 4.5V only
- 38
- Muse Spark Open only
- 25
- Like-for-like categories
- 2 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
Only Muse Spark Open has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5V has a AA Intelligence Index result, 7.6.
Directional only
Web design
Design Arena: websites
No call
Only Muse Spark Open has a Design Arena: websites result, 1317.
Directional only
General
Vals Index
No call
Only Muse Spark Open has a Vals Index result, 48.6%.
Directional only
Long documents
Listed context window
Muse Spark Open
Muse Spark Open takes 1.05M tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
GLM 4.5V $0.0015, Muse Spark Open $0.0034 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Muse Spark Open 47.4 from 4 benchmarks.
- Widest category
- General, 78.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Muse Spark Open 78.3%, 78.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Muse Spark Open $4.25 per million output tokens, 2.4x apart.
- Context window
- GLM 4.5V takes 66K tokens, Muse Spark Open 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 4.5V | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 73.6% | Only Muse Spark Open measured |
| LMArena WebDev | Not measured | 1528 | Only Muse Spark Open measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 6.6% | Only Muse Spark Open measured |
| Vibe Code Bench (Vals AI) | Not measured | 76.2% | Only Muse Spark Open measured |
| Code migration (Vals AI) | Not measured | 21.2% | Only Muse Spark Open measured |
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| SciCode | Not measured | 58.4% | Only Muse Spark Open measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| ALE-Bench | Not measured | 1590.1 | Only Muse Spark Open measured |
| LMArena Text: coding | 1347 | Not measured | Only GLM 4.5V measured |
| LMArena Text: software and IT | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena WebDev: React | Not measured | 1529 | Only Muse Spark Open measured |
| LMArena WebDev: HTML | Not measured | 1495 | Only Muse Spark Open measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | Not measured | Only GLM 4.5V measured |
| Epoch Capabilities Index | Not measured | 155.1 | Only Muse Spark Open measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 80.3% | Only Muse Spark Open measured |
| LiveBench browser | Not measured | 36.0% | Only Muse Spark Open measured |
| LiveBench long agentic | Not measured | 18.9% | Only Muse Spark Open measured |
| Finance Agent (Vals AI) | Not measured | 61.3% | Only Muse Spark Open measured |
| Tax Agent Bench (Vals AI) | Not measured | 26.3% | Only Muse Spark Open measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only Muse Spark Open measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Spark Open measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 53.7% | Only Muse Spark Open measured |
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| APEX-Agents | Not measured | 33.4% | Only Muse Spark Open 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 | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 73.7% | Only Muse Spark Open measured |
| GPQA Diamond (Artificial Analysis run) | 68.4% | 89.0% | Muse Spark Open |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | 43.9% | Muse Spark Open |
| LMArena Text: hard prompts | 1335 | Not measured | Only GLM 4.5V measured |
| LMArena Text: expert | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | 1357 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text: math | 1354 | Not measured | Only GLM 4.5V measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | Not measured | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only GLM 4.5V measured |
| LMArena Text: legal and government | 1338 | Not measured | Only GLM 4.5V measured |
| LMArena Text: business and finance | 1309 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1317 | Only Muse Spark Open measured |
| Design Arena: game dev | Not measured | 1294 | Only Muse Spark Open measured |
| LiveBench design | Not measured | 60.3% | Only Muse Spark Open measured |
| Neither model has a result on the other 6 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Muse Spark Open | Leader |
|---|---|---|---|
| Vals Index | Not measured | 48.6% | Only Muse Spark Open measured |
| LiveBench data analysis | Not measured | 60.2% | Only Muse Spark Open measured |
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | 78.3% | Muse Spark Open |
| LMArena Text | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: instruction following | 1311 | Not measured | Only GLM 4.5V measured |
| LMArena Text: creative writing | 1294 | Not measured | Only GLM 4.5V measured |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | Not measured | Only GLM 4.5V measured |
| LMArena Text: non-English | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Chinese | 1337 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Spanish | 1336 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Russian | 1298 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Polish | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: longer queries | 1305 | Not measured | Only GLM 4.5V measured |
| LMArena Text: writing and language | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | 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 4.5V costs less
Repository review
50K input + 3K output tokens
GLM 4.5V costs less
Agent loop
200K input + 10K output tokens
GLM 4.5V 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 4.5V | Meta Muse Spark Open |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | Open weights |
| Released | August 11, 2025 | October 15, 2026 |
| Context window | 66K | 1.05M |
| Input $/Mtok | $0.60 | $1.25 |
| Output $/Mtok | $1.80 | $4.25 |
| Coding index | Not benchmarked | 47.4 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5V better than Muse Spark Open for coding?
Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 2.4x cheaper ($1.80 vs $4.25 per million).
Which is cheaper, GLM 4.5V or Muse Spark Open?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Muse Spark Open?
Muse Spark Open, with 1.05M tokens against 66K.
How many benchmarks is the GLM 4.5V vs Muse Spark Open comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Muse Spark Open's on 4. Where only one model has a result, the page says so instead of naming a winner.