Model comparison
GLM-4.7-Flash vs Llama 3-8B
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 25.5 on the Noometry Index.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7-Flash leads 35.5 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 1.9% for Llama 3-8B.
Side by side
| GLM-4.7-Flash | Llama 3-8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 38.8 | 25.5 |
| Released | 2026-01-19 | 2024-04-18 |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 34 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Llama 3-8B: 31.0 (#289)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1383 | 1152 |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Reasoning GLM-4.7-Flash leads
GLM-4.7-Flash: 20.9 (#229), Llama 3-8B: 14.3 (#326)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| LMArena Hard Prompts | 1356 | 1133 |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| Epoch Capabilities Index | — | 116.45 |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Llama 3-8B: 8.8 (#323)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 1.9% |
| LMArena Math | 1355 | 1151 |
| MATH Level 5 | — | 6.1% |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.5 (#184), Llama 3-8B: 7.8 (#308)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 60.5% | 26.1% |
| LMArena Expert | 1357 | 1113 |
| Vectara Hallucination Rate | 9.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Llama 3-8B: 30.8 (#261)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1330 | 1098 |
| LMArena Chinese | 1403 | 1076 |
| LMArena French | 1332 | 1159 |
| LMArena German | 1337 | 1104 |
| LMArena Korean | 1283 | 1004 |
| LMArena Russian | 1332 | 1109 |
| LMArena Spanish | 1350 | 1173 |
| LMArena Japanese | — | 967 |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Llama 3-8B: 58.4 (#260)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1127 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), Llama 3-8B: 34.2 (#251)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1345 | 1128 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.4 (#210), Llama 3-8B: 37.5 (#256)
| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Text | 1351 | 1166 |
| LMArena Creative Writing | 1297 | 1150 |
| LMArena Multi-Turn | 1342 | 1152 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Llama 3-8B?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 25.5 on the Noometry Index.
Is GLM-4.7-Flash or Llama 3-8B better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 31.0 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Llama 3-8B share?
19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Llama 3-8B has 34.