Model comparison
GLM-5.3-Flash vs Laguna M.1
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 31.9 on the Noometry Index.
Last verified . 3 shared benchmarks.
PoolsideSummary
- They share 3 benchmarks with published results for both. GLM-5.3-Flash scores higher in 3 categories and Laguna M.1 in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.1 to 20.3.
- The biggest single-benchmark swing is Surface Evolver Bench: 52.5% for GLM-5.3-Flash and 15.6% for Laguna M.1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.3-Flash | Laguna M.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Poolside |
| Noometry Index | 49.6 | 31.9 |
| Released | 2026-08-20 | 2026-04-28 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 36 | 3 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), Laguna M.1: 36.4 (#195)
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena WebDev | 1614 | 1348 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SciCode | 51.6% | — |
| LMArena Coding | 1506 | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.3 (#41), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 31.7 (#69), Laguna M.1: 21.9 (#187)
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| Surface Evolver Bench | 52.5% | 15.6% |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LMArena Hard Prompts | 1490 | — |
| Mystery Game Puzzles | 8% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.9 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#38), Laguna M.1: 20.3 (#276)
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| ProofBench | 21% | 0% |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| LMArena Math | 1499 | — |
Knowledge Not comparable
GLM-5.3-Flash: 58.4 (#33), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1512 | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.6 (#25), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena Vision | 1304 | — |
Multilingual Not comparable
GLM-5.3-Flash: 56.2 (#24), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1529 | — |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Russian | 1472 | — |
| LMArena Spanish | 1471 | — |
Instruction Following Not comparable
GLM-5.3-Flash: 77.8 (#17), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena Instruction Following | 1480 | — |
Long Context Not comparable
GLM-5.3-Flash: 45.3 (#41), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference Not comparable
GLM-5.3-Flash: 65.4 (#49), Laguna M.1: —
| Benchmark | GLM-5.3-Flash | Laguna M.1 |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1440 | — |
| LMArena Multi-Turn | 1471 | — |
Frequently asked questions
Is GLM-5.3-Flash better than Laguna M.1?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 31.9 on the Noometry Index.
Is GLM-5.3-Flash or Laguna M.1 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 36.4 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3-Flash and Laguna M.1 share?
3 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Laguna M.1 has 3.