Model comparison
GLM-5.3 vs Muse Spark 1.1
GLM-5.3 is the stronger model overall, scoring 54.8 to 49.9 on the Noometry Index.
Last verified . 30 shared benchmarks.
MetaSummary
- They share 30 benchmarks with published results for both. GLM-5.3 scores higher in 7 categories and Muse Spark 1.1 in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 45.5.
- The biggest single-benchmark swing is APEX-Agents: 56.6% for GLM-5.3 and 31.8% for Muse Spark 1.1.
- Muse Spark 1.1 is cheaper at $1.25 / $4.25 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- Muse Spark 1.1 accepts more context: 1.05M tokens versus 1M.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | Muse Spark 1.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 54.8 | 49.9 |
| Released | 2026-08-14 | 2026-04-08 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $1.25 |
| Output $ / M tokens | $4.40 | $4.25 |
| Results tracked | 42 | 37 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Muse Spark 1.1: 51.3 (#40)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| DeepSWE | 69% | 53.3% |
| LMArena WebDev | 1622 | 1542 |
| SciCode | 59% | 58.8% |
| LMArena Coding | 1496 | 1498 |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), Muse Spark 1.1: 30.8 (#73)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| APEX-Agents | 56.6% | 31.8% |
| Vending-Bench 2 | 8,164 | 6,520 |
| τ²-bench Banking | — | 40.5% |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
Reasoning Muse Spark 1.1 leads
GLM-5.3: 46.1 (#46), Muse Spark 1.1: 47.1 (#44)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 84.9% |
| CritPt | 19.1% | 15.1% |
| LMArena Hard Prompts | 1489 | 1486 |
| DTBench | 87.7% | 94.4% |
| LMCA | 55.5% | 49.9% |
| Epoch Capabilities Index | 155.61 | 154.21 |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Muse Spark 1.1: 45.5 (#76)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| ProofBench | 49% | 39% |
| LMArena Math | 1489 | 1483 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Muse Spark 1.1: 53.1 (#59)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| SimpleQA Verified | 41% | 57.8% |
| LMArena Expert | 1516 | 1478 |
| GPQA Diamond | 90.9% | — |
Multimodal Not comparable
GLM-5.3: —, Muse Spark 1.1: 42.6 (#29)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |
Multilingual Muse Spark 1.1 leads
GLM-5.3: 55.7 (#28), Muse Spark 1.1: 56.7 (#17)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1457 | 1472 |
| LMArena Chinese | 1528 | 1518 |
| LMArena French | 1499 | 1494 |
| LMArena German | 1499 | 1466 |
| LMArena Japanese | 1453 | 1451 |
| LMArena Korean | 1472 | 1458 |
| LMArena Russian | 1463 | 1483 |
| LMArena Spanish | 1460 | 1464 |
Instruction Following Too close to call
GLM-5.3: 77.5 (#23), Muse Spark 1.1: 76.5 (#39)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1457 |
Long Context Too close to call
GLM-5.3: 45.4 (#41), Muse Spark 1.1: 44.8 (#58)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1482 | 1462 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Muse Spark 1.1: 73.4 (#11)
| Benchmark | GLM-5.3 | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1471 | 1479 |
| LMArena Creative Writing | 1457 | 1437 |
| EQ-Bench Creative Writing | 2075 | 1927 |
| LMArena Multi-Turn | 1472 | 1485 |
| EQ-Bench 4 | — | 1260 |
Frequently asked questions
Is GLM-5.3 better than Muse Spark 1.1?
GLM-5.3 is the stronger model overall, scoring 54.8 to 49.9 on the Noometry Index.
Which is cheaper, GLM-5.3 or Muse Spark 1.1?
Muse Spark 1.1 is cheaper. It lists at $1.25 per million input tokens and $4.25 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Muse Spark 1.1 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 51.3 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3 and Muse Spark 1.1 share?
30 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Muse Spark 1.1 has 37.