Model comparison
GLM-4.6V vs Llama 3.1-405B
GLM-4.6V is the stronger model overall, scoring 40.9 to 30.4 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 3.1-405B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 39.1.
Side by side
| GLM-4.6V | Llama 3.1-405B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 40.9 | 30.4 |
| Released | 2025-12-08 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 33K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 12 | 42 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.2 (#128), Llama 3.1-405B: 32.7 (#257)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Coding | 1390 | 1291 |
| WeirdML | — | 21.4% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Llama 3.1-405B: 20.9 (#131)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| TheAgentCompany | — | 7.4% |
| Cybench | — | 7.5% |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), Llama 3.1-405B: 16.4 (#293)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1270 |
| SimpleBench | — | 23% |
| Kagi LLM Benchmark | — | 45% |
| DTBench | — | 61.4% |
| BIG-Bench Hard | — | 82.9% |
| Epoch Capabilities Index | — | 128.76 |
| ForecastBench | — | 59.9 |
| HellaSwag | — | 89.2% |
| PIQA | — | 85.9% |
| WinoGrande | — | 89.2% |
Math Not comparable
GLM-4.6V: —, Llama 3.1-405B: 17.3 (#283)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 9.7% |
| Omni-MATH | — | 24.9% |
| LMArena Math | — | 1281 |
| MATH Level 5 | — | 49.8% |
Knowledge GLM-4.6V leads
GLM-4.6V: 37.4 (#148), Llama 3.1-405B: 30.4 (#221)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Expert | 1372 | 1243 |
| GPQA Diamond | — | 50.9% |
| MMLU-Pro | — | 72.3% |
| Confabulations | — | 17.6% |
| GPQA (HELM) | — | 52.2% |
| ARC (AI2) Challenge | — | 95.3% |
| MMLU | — | 84.5% |
| TriviaQA | — | 82.7% |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), Llama 3.1-405B: —
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.8 (#135), Llama 3.1-405B: 40.9 (#209)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Non-English | 1359 | 1248 |
| LMArena Chinese | 1426 | 1242 |
| LMArena Russian | 1341 | 1265 |
| LMArena French | — | 1278 |
| LMArena German | — | 1252 |
| LMArena Japanese | — | 1208 |
| LMArena Korean | — | 1184 |
| LMArena Spanish | — | 1259 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.6 (#145), Llama 3.1-405B: 66.1 (#209)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Instruction Following | 1351 | 1259 |
| IFEval | — | 81.1% |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#138), Llama 3.1-405B: 38.4 (#192)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Longer Query | 1358 | 1266 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.8 (#132), Llama 3.1-405B: 39.1 (#246)
| Benchmark | GLM-4.6V | Llama 3.1-405B |
|---|---|---|
| LMArena Text | 1377 | 1284 |
| LMArena Creative Writing | 1347 | 1262 |
| LMArena Multi-Turn | 1359 | 1297 |
| EQ-Bench Creative Writing | — | 870 |
| WildBench | — | 78.3% |
Frequently asked questions
Is GLM-4.6V better than Llama 3.1-405B?
GLM-4.6V is the stronger model overall, scoring 40.9 to 30.4 on the Noometry Index.
Is GLM-4.6V or Llama 3.1-405B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 32.7 in the Noometry coding category.
How many benchmarks do GLM-4.6V and Llama 3.1-405B share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.1-405B has 42.