Model comparison
GLM-4.7 vs Llama 3.1-70B
GLM-4.7 is the stronger model overall, scoring 41.7 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.5× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7 leads 61.1 to 35.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 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 3.1-70B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.7 | 29.4 |
| Released | 2025-12-22 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.60 | $0.40 |
| Output $ / M tokens | $2.20 | $0.40 |
| Results tracked | 36 | 35 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 43.6 (#74), Llama 3.1-70B: 29.8 (#292)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1454 | 1260 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.4 (#96), Llama 3.1-70B: 24.9 (#104)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 23.7 (#161), Llama 3.1-70B: 20.8 (#215)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1241 |
| Epoch Capabilities Index | 143.53 | 125.91 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
Math GLM-4.7 leads
GLM-4.7: 37.7 (#92), Llama 3.1-70B: 12.7 (#298)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 3.6% |
| LMArena Math | 1423 | 1252 |
| ProofBench | 6% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#74), Llama 3.1-70B: 24.1 (#264)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 83.3% | 44.2% |
| LMArena Expert | 1424 | 1209 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multilingual GLM-4.7 leads
GLM-4.7: 53.0 (#75), Llama 3.1-70B: 38.9 (#220)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1417 | 1219 |
| LMArena Chinese | 1495 | 1215 |
| LMArena French | 1432 | 1260 |
| LMArena German | 1426 | 1222 |
| LMArena Japanese | 1439 | 1132 |
| LMArena Korean | 1401 | 1140 |
| LMArena Russian | 1423 | 1234 |
| LMArena Spanish | 1433 | 1253 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.6 (#90), Llama 3.1-70B: 65.5 (#218)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1411 | 1231 |
| IFEval | — | 82.1% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#111), Llama 3.1-70B: 37.6 (#208)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1432 | 1241 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 61.1 (#89), Llama 3.1-70B: 35.6 (#262)
| Benchmark | GLM-4.7 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1436 | 1261 |
| LMArena Creative Writing | 1401 | 1232 |
| EQ-Bench Creative Writing | 1413 | 784 |
| LMArena Multi-Turn | 1446 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GLM-4.7 better than Llama 3.1-70B?
GLM-4.7 is the stronger model overall, scoring 41.7 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.5× 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 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Llama 3.1-70B better for coding?
GLM-4.7 scores higher on coding benchmarks: 43.6 versus 29.8 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 3.1-70B share?
21 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 3.1-70B has 35.