Model comparison
GLM-4.7 vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 42.0 on the Noometry Index. GLM-4.7 costs 2.0× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GLM-4.7 scores higher in 1 category and Muse Spark 1.3 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 38.6.
- The biggest single-benchmark swing is ProofBench: 6% for GLM-4.7 and 58% for Muse Spark 1.3.
- GLM-4.7 is cheaper at $0.60 / $2.20 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 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 42.0 | 54.8 |
| Released | 2025-12-22 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.60 | $1.25 |
| Output $ / M tokens | $2.20 | $4.25 |
| Results tracked | 36 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
GLM-4.7: 44.0 (#79), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1435 | 1657 |
| SciCode | 45.1% | 59.7% |
| LMArena Coding | 1454 | 1514 |
| CursorBench | — | 41.6% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Muse Spark 1.3 leads
GLM-4.7: 26.5 (#103), Muse Spark 1.3: 38.6 (#30)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 2,377 | — |
Reasoning Muse Spark 1.3 leads
GLM-4.7: 24.3 (#164), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| CritPt | 1.7% | 26% |
| Chess Puzzles | 6% | 38% |
| LMArena Hard Prompts | 1443 | 1503 |
| Epoch Capabilities Index | 143.51 | 156.75 |
| SimpleBench | 47.7% | — |
| NYT Connections (extended) | — | 85.1% |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |
Math Muse Spark 1.3 leads
GLM-4.7: 38.6 (#135), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 99.2% |
| ProofBench | 6% | 58% |
| LMArena Math | 1423 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1424 | 1516 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
Multimodal Not comparable
GLM-4.7: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GLM-4.7: 52.8 (#79), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1417 | 1481 |
| LMArena Chinese | 1495 | 1529 |
| LMArena French | 1432 | 1524 |
| LMArena German | 1424 | 1515 |
| LMArena Japanese | 1439 | 1474 |
| LMArena Korean | 1399 | 1501 |
| LMArena Russian | 1423 | 1490 |
| LMArena Spanish | 1434 | 1490 |
Instruction Following Muse Spark 1.3 leads
GLM-4.7: 74.4 (#95), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1411 | 1477 |
Long Context Muse Spark 1.3 leads
GLM-4.7: 42.8 (#116), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1432 | 1488 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference Muse Spark 1.3 leads
GLM-4.7: 60.9 (#93), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GLM-4.7 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1435 | 1490 |
| LMArena Creative Writing | 1401 | 1455 |
| EQ-Bench Creative Writing | 1413 | 1906 |
| LMArena Multi-Turn | 1446 | 1482 |
Frequently asked questions
Is GLM-4.7 better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 42.0 on the Noometry Index. GLM-4.7 costs 2.0× 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 or Muse Spark 1.3?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is GLM-4.7 or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.7 and Muse Spark 1.3 share?
25 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Muse Spark 1.3 has 37.