Model comparison
GLM-4.7-Flash vs Llama2 70b Steerlm Chat
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 31.8 on the Noometry Index.
Last verified . 9 shared benchmarks.
NVIDIASummary
- They share 9 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Llama2 70b Steerlm Chat in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GLM-4.7-Flash leads 46.5 to 28.8.
Side by side
| GLM-4.7-Flash | Llama2 70b Steerlm Chat | |
|---|---|---|
| Provider | Z.ai (Zhipu) | NVIDIA |
| Noometry Index | 38.8 | 31.8 |
| Released | 2026-01-19 | — |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 9 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Llama2 70b Steerlm Chat: 29.9 (#300)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Coding | 1383 | 1025 |
Reasoning Too close to call
GLM-4.7-Flash: 20.9 (#229), Llama2 70b Steerlm Chat: 20.0 (#246)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1047 |
| Chess Puzzles | 0% | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Llama2 70b Steerlm Chat: 31.3 (#226)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Math | 1355 | 1072 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
Knowledge Not comparable
GLM-4.7-Flash: 35.5 (#184), Llama2 70b Steerlm Chat: —
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
| LMArena Expert | 1357 | — |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Llama2 70b Steerlm Chat: 28.8 (#270)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Non-English | 1330 | 1063 |
| LMArena Chinese | 1403 | — |
| LMArena French | 1332 | — |
| LMArena German | 1337 | — |
| LMArena Korean | 1283 | — |
| LMArena Russian | 1332 | — |
| LMArena Spanish | 1350 | — |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Llama2 70b Steerlm Chat: 54.2 (#279)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Instruction Following | 1327 | 1060 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), Llama2 70b Steerlm Chat: 30.4 (#288)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Longer Query | 1345 | 998 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.4 (#210), Llama2 70b Steerlm Chat: 31.6 (#283)
| Benchmark | GLM-4.7-Flash | Llama2 70b Steerlm Chat |
|---|---|---|
| LMArena Text | 1351 | 1098 |
| LMArena Creative Writing | 1297 | 1091 |
| LMArena Multi-Turn | 1342 | 1058 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Llama2 70b Steerlm Chat?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 31.8 on the Noometry Index.
Is GLM-4.7-Flash or Llama2 70b Steerlm Chat better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 29.9 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Llama2 70b Steerlm Chat share?
9 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Llama2 70b Steerlm Chat has 9.