Model comparison
GLM-4.6 vs Granite 3.0 2b Instruct
GLM-4.6 is the stronger model overall, scoring 41.4 to 30.8 on the Noometry Index.
Last verified . 12 shared benchmarks.
Summary
- They share 12 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and Granite 3.0 2b Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 29.6.
Side by side
| GLM-4.6 | Granite 3.0 2b Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | IBM |
| Noometry Index | 41.4 | 30.8 |
| Released | 2025-09-30 | — |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 13 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Granite 3.0 2b Instruct: 28.3 (#316)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Coding | 1449 | 1090 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 20.5% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Granite 3.0 2b Instruct: —
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Granite 3.0 2b Instruct: 20.5 (#235)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1073 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Granite 3.0 2b Instruct: 32.2 (#217)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Math | 1432 | 1117 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), Granite 3.0 2b Instruct: 29.0 (#241)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Expert | 1431 | 1064 |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), Granite 3.0 2b Instruct: 27.0 (#278)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Non-English | 1426 | 1033 |
| LMArena Chinese | 1499 | 1070 |
| LMArena Russian | 1419 | 1045 |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Granite 3.0 2b Instruct: 53.9 (#284)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Instruction Following | 1410 | 1056 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), Granite 3.0 2b Instruct: 32.5 (#268)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Longer Query | 1422 | 1070 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Granite 3.0 2b Instruct: 29.6 (#292)
| Benchmark | GLM-4.6 | Granite 3.0 2b Instruct |
|---|---|---|
| LMArena Text | 1440 | 1080 |
| LMArena Creative Writing | 1411 | 1046 |
| LMArena Multi-Turn | 1427 | 1053 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Granite 3.0 2b Instruct?
GLM-4.6 is the stronger model overall, scoring 41.4 to 30.8 on the Noometry Index.
Is GLM-4.6 or Granite 3.0 2b Instruct better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 28.3 in the Noometry coding category.
How many benchmarks do GLM-4.6 and Granite 3.0 2b Instruct share?
12 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Granite 3.0 2b Instruct has 13.