Model comparison
GLM-4.6 vs Llama 3.2 1B
GLM-4.6 is the stronger model overall, scoring 40.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 14× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. GLM-4.6 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 writing & preference, where GLM-4.6 leads 61.3 to 21.4.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 10.8% 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-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 60K.
Side by side
| GLM-4.6 | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 40.9 | 19.9 |
| Released | 2025-09-30 | 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 | 29 | 22 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 39.9 (#137), Llama 3.2 1B: 20.6 (#333)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1449 | 1071 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.1 (#60), Llama 3.2 1B: 14.5 (#142)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 10.8% |
| Terminal-Bench | 24.5% | — |
| BALROG | — | 6.6% |
Reasoning GLM-4.6 leads
GLM-4.6: 23.1 (#168), Llama 3.2 1B: 15.6 (#304)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1044 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| Epoch Capabilities Index | — | 101.95 |
Math GLM-4.6 leads
GLM-4.6: 36.4 (#115), Llama 3.2 1B: 9.7 (#308)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Math | 1431 | 1086 |
| OTIS Mock AIME 2024-2025 | — | 0.6% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), Llama 3.2 1B: 7.2 (#307)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Expert | 1432 | 1007 |
| GPQA Diamond | — | 23.9% |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), Llama 3.2 1B: 23.9 (#287)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1426 | 973 |
| LMArena Chinese | 1500 | 958 |
| LMArena German | 1447 | 1014 |
| LMArena Russian | 1418 | 941 |
| LMArena French | 1459 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), Llama 3.2 1B: 52.6 (#285)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1031 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), Llama 3.2 1B: 31.9 (#269)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1423 | 1050 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), Llama 3.2 1B: 21.4 (#305)
| Benchmark | GLM-4.6 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1440 | 1055 |
| LMArena Creative Writing | 1411 | 1033 |
| EQ-Bench Creative Writing | 1411 | 200 |
| LMArena Multi-Turn | 1427 | 1030 |
Frequently asked questions
Is GLM-4.6 better than Llama 3.2 1B?
GLM-4.6 is the stronger model overall, scoring 40.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 14× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or Llama 3.2 1B better for coding?
GLM-4.6 scores higher on coding benchmarks: 39.9 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 60K.
How many benchmarks do GLM-4.6 and Llama 3.2 1B share?
15 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Llama 3.2 1B has 22.