Model comparison
GLM-5.3-Flash vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 8.4× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
MetaSummary
- They share 32 benchmarks with published results for both. GLM-5.3-Flash scores higher in 2 categories and Muse Spark 1.3 in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 53.3.
- The biggest single-benchmark swing is ProofBench: 21% for GLM-5.3-Flash and 58% for Muse Spark 1.3.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 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 1M.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.8 | 54.8 |
| Released | 2026-08-20 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.15 | $1.25 |
| Output $ / M tokens | $0.50 | $4.25 |
| Results tracked | 40 | 37 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Muse Spark 1.3 leads
GLM-5.3-Flash: 53.1 (#31), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| CursorBench | 36.8% | 41.6% |
| LMArena WebDev | 1609 | 1657 |
| SciCode | 51.6% | 59.7% |
| LMArena Coding | 1508 | 1514 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Muse Spark 1.3 leads
GLM-5.3-Flash: 34.2 (#47), Muse Spark 1.3: 38.6 (#30)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | 52.8% | 57.8% |
| GDP.pdf | 14% | 27.6% |
Reasoning Muse Spark 1.3 leads
GLM-5.3-Flash: 48.0 (#42), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| CritPt | 15.4% | 26% |
| Chess Puzzles | 14% | 38% |
| LMArena Hard Prompts | 1491 | 1503 |
| Mystery Game Puzzles | 8% | 25% |
| Bench to the Future 3 | 0.15 | 0.14 |
| Epoch Capabilities Index | 151.88 | 156.75 |
| ARC-AGI-2 | 65.8% | — |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 91% | — |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Surface Evolver Bench | 52.5% | — |
Math Muse Spark 1.3 leads
GLM-5.3-Flash: 53.3 (#47), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 74.4% |
| FrontierMath Tier 4 | 17.1% | 46.3% |
| OTIS Mock AIME 2024-2025 | 93.9% | 99.2% |
| ProofBench | 21% | 58% |
| LMArena Math | 1500 | 1494 |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1513 | 1516 |
| GPQA Diamond | 90.2% | — |
Multimodal Too close to call
GLM-5.3-Flash: 42.8 (#27), Muse Spark 1.3: 43.7 (#22)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | 1296 | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GLM-5.3-Flash: 56.0 (#25), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1462 | 1481 |
| LMArena Chinese | 1527 | 1529 |
| LMArena French | 1496 | 1524 |
| LMArena German | 1470 | 1515 |
| LMArena Japanese | 1429 | 1474 |
| LMArena Korean | 1446 | 1501 |
| LMArena Russian | 1469 | 1490 |
| LMArena Spanish | 1471 | 1490 |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1477 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1482 | 1488 |
Writing & Preference Muse Spark 1.3 leads
GLM-5.3-Flash: 65.3 (#50), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GLM-5.3-Flash | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1471 | 1490 |
| LMArena Creative Writing | 1442 | 1455 |
| LMArena Multi-Turn | 1467 | 1482 |
| EQ-Bench Creative Writing | — | 1906 |
Frequently asked questions
Is GLM-5.3-Flash better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 8.4× 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-5.3-Flash or Muse Spark 1.3?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is GLM-5.3-Flash or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and Muse Spark 1.3 share?
32 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Muse Spark 1.3 has 37.