Model comparison
GLM-4.7-Flash vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 14× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and Muse Spark 1.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 36.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 99.2% for Muse Spark 1.3.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.3.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 38.8 | 54.8 |
| Released | 2026-01-19 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.06 | $1.25 |
| Output $ / M tokens | $0.40 | $4.25 |
| Results tracked | 21 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
GLM-4.7-Flash: 40.6 (#135), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Coding | 1383 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| SciCode | — | 59.7% |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Muse Spark 1.3: 38.6 (#30)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
Reasoning Muse Spark 1.3 leads
GLM-4.7-Flash: 20.9 (#229), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| Chess Puzzles | 0% | 38% |
| LMArena Hard Prompts | 1356 | 1503 |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 26% |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 156.75 |
Math Muse Spark 1.3 leads
GLM-4.7-Flash: 36.1 (#173), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 99.2% |
| LMArena Math | 1355 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
Knowledge Muse Spark 1.3 leads
GLM-4.7-Flash: 35.5 (#184), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1357 | 1516 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GLM-4.7-Flash: 46.5 (#158), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1330 | 1481 |
| LMArena Chinese | 1403 | 1529 |
| LMArena French | 1332 | 1524 |
| LMArena German | 1337 | 1515 |
| LMArena Korean | 1283 | 1501 |
| LMArena Russian | 1332 | 1490 |
| LMArena Spanish | 1350 | 1490 |
| LMArena Japanese | — | 1474 |
Instruction Following Muse Spark 1.3 leads
GLM-4.7-Flash: 70.1 (#167), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1477 |
Long Context Muse Spark 1.3 leads
GLM-4.7-Flash: 40.9 (#148), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1345 | 1488 |
Writing & Preference Muse Spark 1.3 leads
GLM-4.7-Flash: 47.4 (#210), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GLM-4.7-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1351 | 1490 |
| LMArena Creative Writing | 1297 | 1455 |
| EQ-Bench Creative Writing | 1125 | 1906 |
| LMArena Multi-Turn | 1342 | 1482 |
Frequently asked questions
Is GLM-4.7-Flash better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 14× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Muse Spark 1.3?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is GLM-4.7-Flash or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Muse Spark 1.3 share?
19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Muse Spark 1.3 has 37.