Model comparison
GLM-4.6V vs Llama 2-13B
GLM-4.6V is the stronger model overall, scoring 41.3 to 29.6 on the Noometry Index.
Last verified . 11 shared benchmarks.
MetaSummary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 2-13B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.6 to 29.8.
Side by side
| GLM-4.6V | Llama 2-13B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 29.6 |
| Released | 2025-12-08 | 2023-07-18 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 33K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 12 | 32 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Llama 2-13B: 30.9 (#291)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Coding | 1390 | 1062 |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Llama 2-13B: 12.8 (#337)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1051 |
| Chess Puzzles | — | 0% |
| DTBench | — | 42.2% |
| BIG-Bench Hard | — | 58.2% |
| Epoch Capabilities Index | — | 106.17 |
| HellaSwag | — | 80.7% |
| LAMBADA | — | 76.5% |
| PIQA | — | 80.8% |
| WinoGrande | — | 72.8% |
Math Not comparable
GLM-4.6V: —, Llama 2-13B: 31.1 (#229)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Math | — | 1065 |
| GSM8K | — | 36.9% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Llama 2-13B: 28.1 (#249)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Expert | 1371 | 1030 |
| ARC (AI2) Challenge | — | 60.3% |
| BoolQ | — | 82.4% |
| MMLU | — | 55.6% |
| OpenBookQA | — | 57% |
| TriviaQA | — | 79.6% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Llama 2-13B: —
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Vision | 1164 | — |
| ScienceQA | — | 55.8% |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Llama 2-13B: 26.5 (#279)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Non-English | 1359 | 1024 |
| LMArena Chinese | 1425 | 1001 |
| LMArena Russian | 1340 | 1055 |
| LMArena French | — | 1044 |
| LMArena German | — | 1009 |
| LMArena Japanese | — | 894 |
| LMArena Korean | — | 953 |
| LMArena Spanish | — | 1087 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Llama 2-13B: 53.3 (#287)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1045 |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#143), Llama 2-13B: 32.3 (#269)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Longer Query | 1358 | 1064 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Llama 2-13B: 29.8 (#289)
| Benchmark | GLM-4.6V | Llama 2-13B |
|---|---|---|
| LMArena Text | 1377 | 1084 |
| LMArena Creative Writing | 1347 | 1047 |
| LMArena Multi-Turn | 1360 | 1050 |
Frequently asked questions
Is GLM-4.6V better than Llama 2-13B?
GLM-4.6V is the stronger model overall, scoring 41.3 to 29.6 on the Noometry Index.
Is GLM-4.6V or Llama 2-13B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 30.9 in the Noometry coding category.
How many benchmarks do GLM-4.6V and Llama 2-13B share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 2-13B has 32.