Model comparison
GLM-4.7 vs Llama 3.2 1B
GLM-4.7 is the stronger model overall, scoring 42.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 14× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 60K.
Side by side
| GLM-4.7 | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 42.0 | 20.1 |
| Released | 2025-12-22 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 205K | 60K |
| Max output | 131K | 54K |
| Input $ / M tokens | $0.60 | $0.027 |
| Output $ / M tokens | $2.20 | $0.20 |
| Results tracked | 36 | 22 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1454 | 1070 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1443 | 1044 |
| Epoch Capabilities Index | 143.51 | 101.99 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 0.6% |
| LMArena Math | 1423 | 1086 |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 83.3% | 23.9% |
| LMArena Expert | 1424 | 1007 |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1417 | 973 |
| LMArena Chinese | 1495 | 959 |
| LMArena German | 1424 | 1014 |
| LMArena Russian | 1423 | 941 |
| LMArena French | 1432 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Spanish | 1434 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1411 | 1031 |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1432 | 1050 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GLM-4.7 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1435 | 1055 |
| LMArena Creative Writing | 1401 | 1033 |
| EQ-Bench Creative Writing | 1413 | 200 |
| LMArena Multi-Turn | 1446 | 1030 |
Frequently asked questions
Is GLM-4.7 better than Llama 3.2 1B?
GLM-4.7 is the stronger model overall, scoring 42.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 14× 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 3.2 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Llama 3.2 1B better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 60K.
How many benchmarks do GLM-4.7 and Llama 3.2 1B share?
18 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 3.2 1B has 22.