Model comparison
GLM-4.6 vs Laguna M.1
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.5 on the Noometry Index.
Last verified . 1 shared benchmarks.
PoolsideSummary
- They share 1 benchmark with published results for both. GLM-4.6 scores higher in 3 categories and Laguna M.1 in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 39.1 to 21.1.
- Laguna M.1 accepts more context: 262K tokens versus 205K.
Side by side
| GLM-4.6 | Laguna M.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Poolside |
| Noometry Index | 41.4 | 32.5 |
| Released | 2025-09-30 | 2026-04-28 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 3 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Laguna M.1: 36.6 (#204)
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena WebDev | 1340 | 1349 |
| SWE-bench Verified (bash only) | 55.4% | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning Too close to call
GLM-4.6: 23.7 (#172), Laguna M.1: 23.1 (#184)
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| LMArena Hard Prompts | 1440 | — |
| Surface Evolver Bench | — | 15.6% |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Laguna M.1: 21.1 (#283)
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| ProofBench | — | 0% |
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
GLM-4.6: 40.2 (#124), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.3 (#98), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
GLM-4.6: 61.1 (#90), Laguna M.1: —
| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than Laguna M.1?
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.5 on the Noometry Index.
Is GLM-4.6 or Laguna M.1 better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 36.6 in the Noometry coding category.
Which has the bigger context window?
Laguna M.1 does, with 262K tokens against 205K.
How many benchmarks do GLM-4.6 and Laguna M.1 share?
1 benchmark has published results for both models. GLM-4.6 has 29 scored results on Noometry and Laguna M.1 has 3.