Model comparison
GLM-5 vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 46.1 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. GLM-5 scores higher in 1 category 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 46.4.
- The biggest single-benchmark swing is Chess Puzzles: 10% for GLM-5 and 38% for Muse Spark 1.3.
- GLM-5 is cheaper at $1 / $3.20 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-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 46.1 | 54.8 |
| Released | 2026-02-11 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $1.25 |
| Output $ / M tokens | $3.20 | $4.25 |
| Results tracked | 45 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
GLM-5: 49.0 (#52), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1434 | 1657 |
| LMArena Coding | 1461 | 1514 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| CursorBench | — | 41.6% |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 59.7% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Muse Spark 1.3 leads
GLM-5: 31.1 (#71), Muse Spark 1.3: 38.6 (#30)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 57.8% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 4,432 | — |
Reasoning Muse Spark 1.3 leads
GLM-5: 27.6 (#116), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| NYT Connections (extended) | 74.8% | 85.1% |
| Chess Puzzles | 10% | 38% |
| LMArena Hard Prompts | 1452 | 1503 |
| Epoch Capabilities Index | 145.83 | 156.75 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 26% |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 61 | — |
Math Muse Spark 1.3 leads
GLM-5: 46.4 (#71), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 99.2% |
| LMArena Math | 1440 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 58% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1454 | 1516 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GLM-5: 53.7 (#58), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1430 | 1481 |
| LMArena Chinese | 1511 | 1529 |
| LMArena French | 1455 | 1524 |
| LMArena German | 1445 | 1515 |
| LMArena Japanese | 1416 | 1474 |
| LMArena Korean | 1423 | 1501 |
| LMArena Russian | 1436 | 1490 |
| LMArena Spanish | 1454 | 1490 |
Instruction Following Muse Spark 1.3 leads
GLM-5: 75.2 (#67), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1428 | 1477 |
Long Context Too close to call
GLM-5: 44.7 (#60), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1446 | 1488 |
| CL-bench | 18.7% | — |
Writing & Preference Muse Spark 1.3 leads
GLM-5: 66.0 (#38), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GLM-5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1446 | 1490 |
| LMArena Creative Writing | 1439 | 1455 |
| EQ-Bench Creative Writing | 1601 | 1906 |
| LMArena Multi-Turn | 1456 | 1482 |
Frequently asked questions
Is GLM-5 better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 46.1 on the Noometry Index.
Which is cheaper, GLM-5 or Muse Spark 1.3?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is GLM-5 or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 49.0 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-5 and Muse Spark 1.3 share?
23 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Muse Spark 1.3 has 37.