Model comparison
GLM-4.6V vs Llama 13b
GLM-4.6V is the stronger model overall, scoring 41.3 to 24.4 on the Noometry Index.
Last verified . 7 shared benchmarks.
MetaSummary
- They share 7 benchmarks with published results for both. GLM-4.6V scores higher in 5 categories and Llama 13b in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.6 to 13.8.
Side by side
| GLM-4.6V | Llama 13b | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 24.4 |
| Released | 2025-12-08 | 2023-02-24 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 33K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 12 | 21 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Llama 13b: 21.4 (#337)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Coding | 1390 | 683 |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Llama 13b: 14.0 (#329)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Hard Prompts | 1368 | 728 |
| BIG-Bench Hard | — | 37.9% |
| Epoch Capabilities Index | — | 100.58 |
| HellaSwag | — | 79.2% |
| LAMBADA | — | 75.2% |
| PIQA | — | 80.1% |
| WinoGrande | — | 73% |
Math Not comparable
GLM-4.6V: —, Llama 13b: 26.7 (#256)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Math | — | 838 |
| GSM8K | — | 20.6% |
Knowledge Not comparable
GLM-4.6V: 38.0 (#149), Llama 13b: —
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Expert | 1371 | — |
| ARC (AI2) Challenge | — | 52.7% |
| BoolQ | — | 78.7% |
| MMLU | — | 47.7% |
| OpenBookQA | — | 56.4% |
| TriviaQA | — | 77.9% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Llama 13b: —
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Vision | 1164 | — |
| ScienceQA | — | 43.3% |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Llama 13b: 16.6 (#297)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Non-English | 1359 | 819 |
| LMArena Chinese | 1425 | — |
| LMArena Russian | 1340 | — |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Llama 13b: 36.7 (#305)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Instruction Following | 1352 | 781 |
Long Context Not comparable
GLM-4.6V: 41.3 (#143), Llama 13b: —
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Longer Query | 1358 | — |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Llama 13b: 13.8 (#312)
| Benchmark | GLM-4.6V | Llama 13b |
|---|---|---|
| LMArena Text | 1377 | 834 |
| LMArena Creative Writing | 1347 | 794 |
| LMArena Multi-Turn | 1360 | 753 |
Frequently asked questions
Is GLM-4.6V better than Llama 13b?
GLM-4.6V is the stronger model overall, scoring 41.3 to 24.4 on the Noometry Index.
Is GLM-4.6V or Llama 13b better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 21.4 in the Noometry coding category.
How many benchmarks do GLM-4.6V and Llama 13b share?
7 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 13b has 21.