Model comparison
GLM-5.3 vs Llama2 70b Steerlm Chat
GLM-5.3 is the stronger model overall, scoring 54.6 to 31.2 on the Noometry Index.
Last verified . 9 shared benchmarks.
NVIDIASummary
- They share 9 benchmarks with published results for both. GLM-5.3 scores higher in 7 categories and Llama2 70b Steerlm Chat in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.9 to 31.8.
Side by side
| GLM-5.3 | Llama2 70b Steerlm Chat | |
|---|---|---|
| Provider | Z.ai (Zhipu) | NVIDIA |
| Noometry Index | 54.6 | 31.2 |
| Released | 2026-08-14 | — |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.40 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 41 | 9 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.0 (#12), Llama2 70b Steerlm Chat: 29.3 (#295)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Coding | 1499 | 1025 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| LMArena WebDev | 1623 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.3 (#34), Llama2 70b Steerlm Chat: —
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 45.2 (#42), Llama2 70b Steerlm Chat: 19.1 (#245)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1047 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.56 | — |
Math GLM-5.3 leads
GLM-5.3: 62.1 (#27), Llama2 70b Steerlm Chat: 28.8 (#223)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Math | 1493 | 1072 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
Knowledge Not comparable
GLM-5.3: 58.3 (#34), Llama2 70b Steerlm Chat: —
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1512 | — |
Multilingual GLM-5.3 leads
GLM-5.3: 55.9 (#27), Llama2 70b Steerlm Chat: 28.9 (#265)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Non-English | 1457 | 1064 |
| LMArena Chinese | 1523 | — |
| LMArena French | 1499 | — |
| LMArena German | 1505 | — |
| LMArena Japanese | 1438 | — |
| LMArena Korean | 1478 | — |
| LMArena Russian | 1460 | — |
| LMArena Spanish | 1444 | — |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.8 (#15), Llama2 70b Steerlm Chat: 54.5 (#274)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Instruction Following | 1481 | 1061 |
Long Context GLM-5.3 leads
GLM-5.3: 45.5 (#32), Llama2 70b Steerlm Chat: 30.4 (#283)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Longer Query | 1485 | 998 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.9 (#6), Llama2 70b Steerlm Chat: 31.8 (#278)
| Benchmark | GLM-5.3 | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Text | 1473 | 1098 |
| LMArena Creative Writing | 1459 | 1091 |
| LMArena Multi-Turn | 1473 | 1058 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than Llama2 70b Steerlm Chat?
GLM-5.3 is the stronger model overall, scoring 54.6 to 31.2 on the Noometry Index.
Is GLM-5.3 or Llama2 70b Steerlm Chat better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 29.3 in the Noometry coding category.
How many benchmarks do GLM-5.3 and Llama2 70b Steerlm Chat share?
9 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and Llama2 70b Steerlm Chat has 9.