Model comparison
GLM-4.7 vs Llama 3.1-8B
GLM-4.7 is the stronger model overall, scoring 42.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-4.7 | Llama 3.1-8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 42.0 | 23.0 |
| Released | 2025-12-22 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.60 | $0.05 |
| Output $ / M tokens | $2.20 | $0.08 |
| Results tracked | 36 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| SciCode | 45.1% | 13.2% |
| LMArena Coding | 1454 | 1195 |
| LMArena WebDev | 1435 | — |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 399.48 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| CritPt | 1.7% | 0% |
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1443 | 1175 |
| Epoch Capabilities Index | 143.51 | 116.57 |
| SimpleBench | 47.7% | — |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| PIQA | — | 81.2% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 1.7% |
| LMArena Math | 1423 | 1179 |
| ProofBench | 6% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 82.4% |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 83.3% | 27% |
| LMArena Expert | 1424 | 1144 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 40.6% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1417 | 1148 |
| LMArena Chinese | 1495 | 1151 |
| LMArena French | 1432 | 1177 |
| LMArena German | 1424 | 1144 |
| LMArena Japanese | 1439 | 1061 |
| LMArena Korean | 1399 | 1053 |
| LMArena Russian | 1423 | 1158 |
| LMArena Spanish | 1434 | 1169 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1411 | 1159 |
| IFEval | — | 74.3% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1432 | 1182 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GLM-4.7 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1435 | 1187 |
| LMArena Creative Writing | 1401 | 1154 |
| EQ-Bench Creative Writing | 1413 | 713 |
| LMArena Multi-Turn | 1446 | 1172 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GLM-4.7 better than Llama 3.1-8B?
GLM-4.7 is the stronger model overall, scoring 42.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or Llama 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Llama 3.1-8B better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.7 and Llama 3.1-8B share?
24 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 3.1-8B has 43.