Model comparison
GLM-4.6V vs Llama 2-70B
GLM-4.6V is the stronger model overall, scoring 41.3 to 24.4 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 2-70B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6V leads 38.0 to 7.4.
Side by side
| GLM-4.6V | Llama 2-70B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 24.4 |
| 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 | 35 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Llama 2-70B: 31.4 (#286)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Coding | 1390 | 1079 |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Llama 2-70B: 14.4 (#325)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1073 |
| DTBench | — | 41.6% |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| Epoch Capabilities Index | — | 113.79 |
| ForecastBench | — | 51.4 |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |
Math Not comparable
GLM-4.6V: —, Llama 2-70B: 8.1 (#326)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 0% |
| LMArena Math | — | 1091 |
| MATH Level 5 | — | 3.3% |
| GSM8K | — | 69.6% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Llama 2-70B: 7.4 (#310)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Expert | 1371 | 1039 |
| GPQA Diamond | — | 26.3% |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Llama 2-70B: —
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Llama 2-70B: 27.7 (#274)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Non-English | 1359 | 1045 |
| LMArena Chinese | 1425 | 995 |
| LMArena Russian | 1340 | 1083 |
| LMArena French | — | 1090 |
| LMArena German | — | 1041 |
| LMArena Japanese | — | 927 |
| LMArena Korean | — | 964 |
| LMArena Spanish | — | 1143 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Llama 2-70B: 54.9 (#278)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1071 |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#143), Llama 2-70B: 32.3 (#270)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | 1358 | 1062 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Llama 2-70B: 32.3 (#279)
| Benchmark | GLM-4.6V | Llama 2-70B |
|---|---|---|
| LMArena Text | 1377 | 1115 |
| LMArena Creative Writing | 1347 | 1075 |
| LMArena Multi-Turn | 1360 | 1088 |
Frequently asked questions
Is GLM-4.6V better than Llama 2-70B?
GLM-4.6V is the stronger model overall, scoring 41.3 to 24.4 on the Noometry Index.
Is GLM-4.6V or Llama 2-70B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 31.4 in the Noometry coding category.
How many benchmarks do GLM-4.6V and Llama 2-70B share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 2-70B has 35.