Model comparison
GLM-5 vs Muse Spark 1.1
Muse Spark 1.1 is the stronger model overall, scoring 49.9 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 2 categories and Muse Spark 1.1 in 7 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 47.1 to 27.6.
- The biggest single-benchmark swing is τ²-bench Banking: 9.8% for GLM-5 and 40.5% for Muse Spark 1.1.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.1.
- Muse Spark 1.1 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 1.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 46.1 | 49.9 |
| Released | 2026-02-11 | 2026-04-08 |
| 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 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Muse Spark 1.1 leads
GLM-5: 49.0 (#52), Muse Spark 1.1: 51.3 (#40)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena WebDev | 1434 | 1542 |
| LMArena Coding | 1461 | 1498 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 53.3% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 58.8% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Too close to call
GLM-5: 31.1 (#71), Muse Spark 1.1: 30.8 (#73)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| τ²-bench Banking | 9.8% | 40.5% |
| Vending-Bench 2 | 4,432 | 6,520 |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 31.8% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
Reasoning Muse Spark 1.1 leads
GLM-5: 27.6 (#116), Muse Spark 1.1: 47.1 (#44)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| NYT Connections (extended) | 74.8% | 84.9% |
| LMArena Hard Prompts | 1452 | 1486 |
| Epoch Capabilities Index | 145.83 | 154.21 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 15.1% |
| Chess Puzzles | 10% | — |
| DTBench | — | 94.4% |
| LMCA | — | 49.9% |
| Surface Evolver Bench | — | 52.5% |
| ForecastBench | 61 | — |
Math Too close to call
GLM-5: 46.4 (#71), Muse Spark 1.1: 45.5 (#76)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Math | 1440 | 1483 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| ProofBench | — | 39% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Too close to call
GLM-5: 52.3 (#64), Muse Spark 1.1: 53.1 (#59)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1454 | 1478 |
| GPQA Diamond | 87.8% | — |
| SimpleQA Verified | — | 57.8% |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, Muse Spark 1.1: 42.6 (#29)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |
Multilingual Muse Spark 1.1 leads
GLM-5: 53.7 (#58), Muse Spark 1.1: 56.7 (#17)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1430 | 1472 |
| LMArena Chinese | 1511 | 1518 |
| LMArena French | 1455 | 1494 |
| LMArena German | 1445 | 1466 |
| LMArena Japanese | 1416 | 1451 |
| LMArena Korean | 1423 | 1458 |
| LMArena Russian | 1436 | 1483 |
| LMArena Spanish | 1454 | 1464 |
Instruction Following Muse Spark 1.1 leads
GLM-5: 75.2 (#67), Muse Spark 1.1: 76.5 (#39)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1428 | 1457 |
Long Context Too close to call
GLM-5: 44.7 (#60), Muse Spark 1.1: 44.8 (#58)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1446 | 1462 |
| CL-bench | 18.7% | — |
Writing & Preference Muse Spark 1.1 leads
GLM-5: 66.0 (#38), Muse Spark 1.1: 73.4 (#11)
| Benchmark | GLM-5 | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1446 | 1479 |
| LMArena Creative Writing | 1439 | 1437 |
| EQ-Bench Creative Writing | 1601 | 1927 |
| LMArena Multi-Turn | 1456 | 1485 |
| EQ-Bench 4 | — | 1260 |
Frequently asked questions
Is GLM-5 better than Muse Spark 1.1?
Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 46.1 on the Noometry Index.
Which is cheaper, GLM-5 or Muse Spark 1.1?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.
Is GLM-5 or Muse Spark 1.1 better for coding?
Muse Spark 1.1 scores higher on coding benchmarks: 51.3 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and Muse Spark 1.1 share?
23 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Muse Spark 1.1 has 37.