Model comparison
GLM-5 vs Llama 3.1-405B
GLM-5 is the stronger model overall, scoring 46.1 to 30.7 on the Noometry Index.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GLM-5 scores higher in 9 categories and Llama 3.1-405B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5 leads 46.4 to 18.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 and 9.7% for Llama 3.1-405B.
Side by side
| GLM-5 | Llama 3.1-405B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 46.1 | 30.7 |
| Released | 2026-02-11 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $1 | — |
| Output $ / M tokens | $3.20 | — |
| Results tracked | 45 | 42 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Llama 3.1-405B: 33.1 (#262)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| WeirdML | 48.2% | 21.4% |
| LMArena Coding | 1461 | 1291 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Llama 3.1-405B: 21.0 (#140)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| TheAgentCompany | — | 7.4% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Cybench | — | 7.5% |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Llama 3.1-405B: 16.8 (#300)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| SimpleBench | 53.2% | 23% |
| Kagi LLM Benchmark | 75% | 45% |
| LMArena Hard Prompts | 1452 | 1269 |
| Epoch Capabilities Index | 145.83 | 128.75 |
| ForecastBench | 61 | 59.9 |
| ARC-AGI-2 | 4.9% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| DTBench | — | 61.4% |
| BIG-Bench Hard | — | 82.9% |
| HellaSwag | — | 89.2% |
| PIQA | — | 85.9% |
| WinoGrande | — | 89.2% |
Math GLM-5 leads
GLM-5: 46.4 (#71), Llama 3.1-405B: 18.4 (#290)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 9.7% |
| LMArena Math | 1440 | 1281 |
| MathArena Final-Answer Competitions | 65.7% | — |
| Omni-MATH | — | 24.9% |
| MATH Level 5 | — | 49.8% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Llama 3.1-405B: 30.4 (#227)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| GPQA Diamond | 87.8% | 50.9% |
| LMArena Expert | 1454 | 1243 |
| MMLU-Pro | — | 72.3% |
| Confabulations | — | 17.6% |
| Vectara Hallucination Rate | 10.1% | — |
| GPQA (HELM) | — | 52.2% |
| ARC (AI2) Challenge | — | 95.3% |
| MMLU | — | 84.5% |
| TriviaQA | — | 82.7% |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Llama 3.1-405B: 40.7 (#214)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| LMArena Non-English | 1430 | 1248 |
| LMArena Chinese | 1511 | 1242 |
| LMArena French | 1455 | 1279 |
| LMArena German | 1445 | 1252 |
| LMArena Japanese | 1416 | 1208 |
| LMArena Korean | 1423 | 1184 |
| LMArena Russian | 1436 | 1265 |
| LMArena Spanish | 1454 | 1260 |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Llama 3.1-405B: 65.9 (#214)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| LMArena Instruction Following | 1428 | 1259 |
| IFEval | — | 81.1% |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Llama 3.1-405B: 38.4 (#197)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| LMArena Longer Query | 1446 | 1266 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Llama 3.1-405B: 38.9 (#251)
| Benchmark | GLM-5 | Llama 3.1-405B |
|---|---|---|
| LMArena Text | 1446 | 1284 |
| LMArena Creative Writing | 1439 | 1262 |
| EQ-Bench Creative Writing | 1601 | 870 |
| LMArena Multi-Turn | 1456 | 1297 |
| WildBench | — | 78.3% |
Frequently asked questions
Is GLM-5 better than Llama 3.1-405B?
GLM-5 is the stronger model overall, scoring 46.1 to 30.7 on the Noometry Index.
Is GLM-5 or Llama 3.1-405B better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 33.1 in the Noometry coding category.
How many benchmarks do GLM-5 and Llama 3.1-405B share?
25 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Llama 3.1-405B has 42.