Model comparison
GLM-4.6 vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 41.4 on the Noometry Index. GLM-4.6 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 . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and Muse Spark 1.3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 39.1.
- The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 26% for Muse Spark 1.3.
- GLM-4.6 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-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.4 | 54.8 |
| Released | 2025-09-30 | 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 | 29 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
GLM-4.6: 40.1 (#148), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1340 | 1657 |
| SciCode | 38.4% | 59.7% |
| LMArena Coding | 1449 | 1514 |
| SWE-bench Verified (bash only) | 55.4% | — |
| CursorBench | — | 41.6% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Muse Spark 1.3 leads
GLM-4.6: 32.3 (#66), Muse Spark 1.3: 38.6 (#30)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 57.8% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| GDP.pdf | — | 27.6% |
Reasoning Muse Spark 1.3 leads
GLM-4.6: 23.7 (#172), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| CritPt | 1.1% | 26% |
| LMArena Hard Prompts | 1440 | 1503 |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 38% |
| 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-4.6: 39.1 (#111), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Math | 1432 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 99.2% |
| ProofBench | — | 58% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Muse Spark 1.3 leads
GLM-4.6: 40.2 (#124), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1431 | 1516 |
| Vectara Hallucination Rate | 9.5% | — |
Multimodal Not comparable
GLM-4.6: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GLM-4.6: 53.5 (#66), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1426 | 1481 |
| LMArena Chinese | 1499 | 1529 |
| LMArena French | 1459 | 1524 |
| LMArena German | 1447 | 1515 |
| LMArena Japanese | 1393 | 1474 |
| LMArena Korean | 1400 | 1501 |
| LMArena Russian | 1419 | 1490 |
| LMArena Spanish | 1436 | 1490 |
Instruction Following Muse Spark 1.3 leads
GLM-4.6: 74.3 (#98), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1477 |
Long Context Muse Spark 1.3 leads
GLM-4.6: 43.4 (#94), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1422 | 1488 |
Writing & Preference Muse Spark 1.3 leads
GLM-4.6: 61.1 (#90), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GLM-4.6 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1440 | 1490 |
| LMArena Creative Writing | 1411 | 1455 |
| EQ-Bench Creative Writing | 1411 | 1906 |
| LMArena Multi-Turn | 1427 | 1482 |
Frequently asked questions
Is GLM-4.6 better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 41.4 on the Noometry Index. GLM-4.6 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-4.6 or Muse Spark 1.3?
GLM-4.6 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-4.6 or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 40.1 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-4.6 and Muse Spark 1.3 share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Muse Spark 1.3 has 37.