Model comparison
GLM-4.7 vs Llama 13b
GLM-4.7 is the stronger model overall, scoring 42.0 to 24.4 on the Noometry Index.
Last verified . 9 shared benchmarks.
MetaSummary
- They share 9 benchmarks with published results for both. GLM-4.7 scores higher in 6 categories and Llama 13b in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 13.8.
Side by side
| GLM-4.7 | Llama 13b | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 42.0 | 24.4 |
| Released | 2025-12-22 | 2023-02-24 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 36 | 21 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Llama 13b: 21.4 (#337)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Coding | 1454 | 683 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), Llama 13b: —
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Llama 13b: 14.0 (#329)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Hard Prompts | 1443 | 728 |
| Epoch Capabilities Index | 143.51 | 100.58 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| BIG-Bench Hard | — | 37.9% |
| HellaSwag | — | 79.2% |
| LAMBADA | — | 75.2% |
| PIQA | — | 80.1% |
| WinoGrande | — | 73% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Llama 13b: 26.7 (#256)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Math | 1423 | 838 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 20.6% |
Knowledge Not comparable
GLM-4.7: 47.0 (#80), Llama 13b: —
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
| LMArena Expert | 1424 | — |
| ARC (AI2) Challenge | — | 52.7% |
| BoolQ | — | 78.7% |
| MMLU | — | 47.7% |
| OpenBookQA | — | 56.4% |
| TriviaQA | — | 77.9% |
Multimodal Not comparable
GLM-4.7: —, Llama 13b: —
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| ScienceQA | — | 43.3% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Llama 13b: 16.6 (#297)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Non-English | 1417 | 819 |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1434 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Llama 13b: 36.7 (#305)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Instruction Following | 1411 | 781 |
Long Context Not comparable
GLM-4.7: 42.8 (#116), Llama 13b: —
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Llama 13b: 13.8 (#312)
| Benchmark | GLM-4.7 | Llama 13b |
|---|---|---|
| LMArena Text | 1435 | 834 |
| LMArena Creative Writing | 1401 | 794 |
| LMArena Multi-Turn | 1446 | 753 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Llama 13b?
GLM-4.7 is the stronger model overall, scoring 42.0 to 24.4 on the Noometry Index.
Is GLM-4.7 or Llama 13b better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 21.4 in the Noometry coding category.
How many benchmarks do GLM-4.7 and Llama 13b share?
9 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 13b has 21.