Model comparison
GLM-5.3 vs Llama 2-7B
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.1 on the Noometry Index.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Llama 2-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 28.0.
- The biggest single-benchmark swing is Chess Puzzles: 21% for GLM-5.3 and 0% for Llama 2-7B.
Side by side
| GLM-5.3 | Llama 2-7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 54.8 | 29.1 |
| Released | 2026-08-14 | 2023-07-18 |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.40 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 42 | 29 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Llama 2-7B: 29.2 (#307)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Coding | 1496 | 1002 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Llama 2-7B: —
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Llama 2-7B: 15.7 (#312)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1489 | 1009 |
| Epoch Capabilities Index | 155.61 | 99.06 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 39.2% |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Llama 2-7B: 30.7 (#233)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Math | 1489 | 1042 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| GSM8K | — | 16.7% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Llama 2-7B: 28.2 (#248)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Expert | 1516 | 1036 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| MMLU | — | 45.8% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |
Multimodal Not comparable
GLM-5.3: —, Llama 2-7B: —
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| ScienceQA | — | 43.1% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Llama 2-7B: 23.8 (#293)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Non-English | 1457 | 973 |
| LMArena Chinese | 1528 | 973 |
| LMArena French | 1499 | 970 |
| LMArena German | 1499 | 978 |
| LMArena Russian | 1463 | 995 |
| LMArena Spanish | 1460 | 1007 |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Llama 2-7B: 50.8 (#298)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1006 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Llama 2-7B: 30.4 (#287)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | 1482 | 999 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Llama 2-7B: 28.0 (#298)
| Benchmark | GLM-5.3 | Llama 2-7B |
|---|---|---|
| LMArena Text | 1471 | 1053 |
| LMArena Creative Writing | 1457 | 1033 |
| LMArena Multi-Turn | 1472 | 1029 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than Llama 2-7B?
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.1 on the Noometry Index.
Is GLM-5.3 or Llama 2-7B better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 29.2 in the Noometry coding category.
How many benchmarks do GLM-5.3 and Llama 2-7B share?
17 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Llama 2-7B has 29.