GLM 4.7 Flash vs Muse Spark 1.1
Muse Spark 1.1 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 33.7 to 14.9, and GLM 4.7 Flash costs 10.6x less per output token ($0.40 vs $4.25 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
GLM 4.7 Flash or Muse Spark 1.1: which to pick
Only Muse Spark 1.1 has a coding score, 70.3 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 10.6x cheaper ($0.40 vs $4.25 per million).
- Shared results
- 38
- GLM 4.7 Flash only
- 5
- Muse Spark 1.1 only
- 58
- Like-for-like categories
- 7 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 1.1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Muse Spark 1.1
Muse Spark 1.1 leads, 33.7 against 14.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Muse Spark 1.1 has a AA Agentic Index result, 25.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Muse Spark 1.1 has a GPQA Diamond result, 91.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Muse Spark 1.1 has a MMLU Pro result, 88.7%.
Directional only
Web design
Design Arena: websites
Muse Spark 1.1
Muse Spark 1.1 leads, 1270 against 1194.
Same board, both measured
Long documents
Listed context window
Muse Spark 1.1
Muse Spark 1.1 takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Muse Spark 1.1 $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Muse Spark 1.1 has the number, $0.43.
Directional only
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Muse Spark 1.1 70.3 from 3 benchmarks.
- Widest category
- Reasoning, 26.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GLM 4.7 Flash 7.6% against Muse Spark 1.1 46.2%, 38.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Muse Spark 1.1 $4.25 per million output tokens, 10.6x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, Muse Spark 1.1 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 Flash | Bars | Muse Spark 1.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1542 | Only Muse Spark 1.1 measured |
| AA Coding Index | Not measured | 71.3 | Only Muse Spark 1.1 measured |
| LiveCodeBench (Vals AI) | Not measured | 85.9% | Only Muse Spark 1.1 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.0% | Only Muse Spark 1.1 measured |
| Vibe Code Bench (Vals AI) | Not measured | 72.2% | Only Muse Spark 1.1 measured |
| Code migration (Vals AI) | Not measured | 31.1% | Only Muse Spark 1.1 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Muse Spark 1.1 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 69.3% | Only Muse Spark 1.1 measured |
| SkillsBench (Vals AI) | Not measured | 59.2% | Only Muse Spark 1.1 measured |
| SciCode | Not measured | 58.8% | Only Muse Spark 1.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | 1498 | Muse Spark 1.1 |
| LMArena Text: software and IT | 1389 | 1498 | Muse Spark 1.1 |
| LMArena WebDev: React | Not measured | 1532 | Only Muse Spark 1.1 measured |
| LMArena WebDev: HTML | Not measured | 1540 | Only Muse Spark 1.1 measured |
| LMArena WebDev: from an image | Not measured | 1540 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 33.7 | Muse Spark 1.1 |
| Epoch Capabilities Index | Not measured | 154.1 | Only Muse Spark 1.1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 25.8 | Only Muse Spark 1.1 measured |
| Design Arena: agents building web apps | Not measured | 1207 | Only Muse Spark 1.1 measured |
| Design Arena: agents building full-stack apps | Not measured | 1199 | Only Muse Spark 1.1 measured |
| Design Arena: agents building mobile apps | Not measured | 1175 | Only Muse Spark 1.1 measured |
| Design Arena: agents building Android apps | Not measured | 1178 | Only Muse Spark 1.1 measured |
| Design Arena: agents building Godot games | Not measured | 1103 | Only Muse Spark 1.1 measured |
| Design Arena: agents building games | Not measured | 1183 | Only Muse Spark 1.1 measured |
| Design Arena: HTML slides | Not measured | 1195 | Only Muse Spark 1.1 measured |
| Design Arena: PowerPoint slides | Not measured | 1155 | Only Muse Spark 1.1 measured |
| Design Arena: spreadsheets | Not measured | 1042 | Only Muse Spark 1.1 measured |
| Finance Agent (Vals AI) | Not measured | 57.2% | Only Muse Spark 1.1 measured |
| Legal Research Bench (Vals AI) | Not measured | 38.0% | Only Muse Spark 1.1 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.4% | Only Muse Spark 1.1 measured |
| Time Horizon Index (Vals AI) | Not measured | 1.7% | Only Muse Spark 1.1 measured |
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| APEX-Agents | Not measured | 31.8% | Only Muse Spark 1.1 measured |
| Vending-Bench 2 | Not measured | $6,520 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 91.2% | Only Muse Spark 1.1 measured |
| MMMU Pro (Vals AI) | Not measured | 86.6% | Only Muse Spark 1.1 measured |
| GPQA Diamond (Artificial Analysis run) | 58.1% | 89.8% | Muse Spark 1.1 |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 46.2% | Muse Spark 1.1 |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| Chess puzzles | Not measured | 34.0% | Only Muse Spark 1.1 measured |
| LMArena Text: hard prompts | 1356 | 1485 | Muse Spark 1.1 |
| LMArena Text: expert | 1357 | 1478 | Muse Spark 1.1 |
| LMArena Text: hard prompts in English | 1369 | 1480 | Muse Spark 1.1 |
| LMArena Vision: diagrams | Not measured | 1305 | Only Muse Spark 1.1 measured |
| LMArena Vision: homework | Not measured | 1287 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| ProofBench | Not measured | 39.0% | Only Muse Spark 1.1 measured |
| LMArena Text: math | 1355 | 1482 | Muse Spark 1.1 |
| LMArena Text: mathematical work | 1369 | 1487 | Muse Spark 1.1 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.7% | Only Muse Spark 1.1 measured |
| LegalBench (Vals AI) | Not measured | 85.0% | Only Muse Spark 1.1 measured |
| CorpFin (Vals AI) | Not measured | 71.3% | Only Muse Spark 1.1 measured |
| TaxEval (Vals AI) | Not measured | 79.7% | Only Muse Spark 1.1 measured |
| MortgageTax (Vals AI) | Not measured | 66.1% | Only Muse Spark 1.1 measured |
| SimpleQA Verified | Not measured | 57.8% | Only Muse Spark 1.1 measured |
| LMArena Text: science | 1358 | 1484 | Muse Spark 1.1 |
| LMArena Text: medicine and healthcare | 1343 | 1479 | Muse Spark 1.1 |
| LMArena Text: legal and government | 1357 | 1481 | Muse Spark 1.1 |
| LMArena Text: business and finance | 1343 | 1470 | Muse Spark 1.1 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1270 | Muse Spark 1.1 |
| Design Arena: UI components | 1205 | 1289 | Muse Spark 1.1 |
| Design Arena: game dev | 1134 | 1269 | Muse Spark 1.1 |
| Design Arena: data viz | 1123 | 1268 | Muse Spark 1.1 |
| Design Arena: 3D | 1120 | 1260 | Muse Spark 1.1 |
| Design Arena: all code categories | 1177 | 1276 | Muse Spark 1.1 |
| Design Arena: SVG | 1037 | 1235 | Muse Spark 1.1 |
| Design Arena: ASCII art | Not measured | 1271 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 61.3% | Only Muse Spark 1.1 measured |
| SAGE (Vals AI) | Not measured | 45.6% | Only Muse Spark 1.1 measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only Muse Spark 1.1 measured |
| Vals Multimodal Index | Not measured | 66.7% | Only Muse Spark 1.1 measured |
| IFBench | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | 77.7% | Muse Spark 1.1 |
| DTBench | Not measured | 94.4% | Only Muse Spark 1.1 measured |
| LMCA | Not measured | 49.9% | Only Muse Spark 1.1 measured |
| Surface Evolver Bench | Not measured | 52.5% | Only Muse Spark 1.1 measured |
| LMArena Text | 1351 | 1478 | Muse Spark 1.1 |
| LMArena Text: instruction following | 1327 | 1457 | Muse Spark 1.1 |
| LMArena Text: creative writing | 1296 | 1435 | Muse Spark 1.1 |
| LMArena Vision | Not measured | 1293 | Only Muse Spark 1.1 measured |
| LMArena Text: English | 1373 | 1478 | Muse Spark 1.1 |
| LMArena Text: non-English | 1330 | 1471 | Muse Spark 1.1 |
| LMArena Text: Chinese | 1403 | 1515 | Muse Spark 1.1 |
| LMArena Text: German | 1336 | 1469 | Muse Spark 1.1 |
| LMArena Text: Japanese | Not measured | 1450 | Only Muse Spark 1.1 measured |
| LMArena Text: Korean | 1283 | 1459 | Muse Spark 1.1 |
| LMArena Text: French | 1332 | 1492 | Muse Spark 1.1 |
| LMArena Text: Spanish | 1350 | 1464 | Muse Spark 1.1 |
| LMArena Text: Russian | 1333 | 1482 | Muse Spark 1.1 |
| LMArena Text: Polish | 1291 | 1499 | Muse Spark 1.1 |
| LMArena Text: multi-turn | 1342 | 1484 | Muse Spark 1.1 |
| LMArena Text: longer queries | 1345 | 1462 | Muse Spark 1.1 |
| LMArena Text: writing and language | 1320 | 1448 | Muse Spark 1.1 |
| LMArena Text: entertainment and media | 1304 | 1438 | Muse Spark 1.1 |
| LMArena Vision: English | Not measured | 1292 | Only Muse Spark 1.1 measured |
| LMArena Vision: Chinese | Not measured | 1348 | Only Muse Spark 1.1 measured |
| LMArena Vision: reading text | Not measured | 1301 | Only Muse Spark 1.1 measured |
| LMArena Vision: humor | Not measured | 1288 | Only Muse Spark 1.1 measured |
| LMArena Vision: creative writing | Not measured | 1253 | Only Muse Spark 1.1 measured |
| LMArena Document | Not measured | 1465 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | Muse Spark 1.1 | 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 4.7 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 Flash 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 Flash | Meta Muse Spark 1.1 |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | July 16, 2026 |
| Context window | 200K | 1.05M |
| Input $/Mtok | $0.0653 | $1.25 |
| Output $/Mtok | $0.40 | $4.25 |
| Coding index | Not benchmarked | 70.3 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 Flash better than Muse Spark 1.1 for coding?
Only Muse Spark 1.1 has a coding score, 70.3 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 10.6x cheaper ($0.40 vs $4.25 per million).
Which is cheaper, GLM 4.7 Flash or Muse Spark 1.1?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Cost per solved task is known for Muse Spark 1.1 only.
Which has the bigger context window, GLM 4.7 Flash or Muse Spark 1.1?
Muse Spark 1.1, with 1.05M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs Muse Spark 1.1 comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Muse Spark 1.1's on 3. Where only one model has a result, the page says so instead of naming a winner.