Model comparison
GLM-5 vs Llama 3.2 1B
GLM-5 is the stronger model overall, scoring 46.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 22× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. GLM-5 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-5 leads 52.3 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 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 $1 / $3.20 for GLM-5.
- GLM-5 accepts more context: 205K tokens versus 60K.
Side by side
| GLM-5 | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 46.1 | 20.1 |
| Released | 2026-02-11 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 205K | 60K |
| Max output | 131K | 54K |
| Input $ / M tokens | $1 | $0.027 |
| Output $ / M tokens | $3.20 | $0.20 |
| Results tracked | 45 | 22 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1461 | 1070 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| BALROG | — | 6.6% |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 10% | 0% |
| LMArena Hard Prompts | 1452 | 1044 |
| Epoch Capabilities Index | 145.83 | 101.99 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 46.4 (#71), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 0.6% |
| LMArena Math | 1440 | 1086 |
| MathArena Final-Answer Competitions | 65.7% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 87.8% | 23.9% |
| LMArena Expert | 1454 | 1007 |
| Vectara Hallucination Rate | 10.1% | — |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1430 | 973 |
| LMArena Chinese | 1511 | 959 |
| LMArena German | 1445 | 1014 |
| LMArena Russian | 1436 | 941 |
| LMArena French | 1455 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Spanish | 1454 | — |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1428 | 1031 |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1446 | 1050 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GLM-5 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1446 | 1055 |
| LMArena Creative Writing | 1439 | 1033 |
| EQ-Bench Creative Writing | 1601 | 200 |
| LMArena Multi-Turn | 1456 | 1030 |
Frequently asked questions
Is GLM-5 better than Llama 3.2 1B?
GLM-5 is the stronger model overall, scoring 46.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 22× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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-5 lists at $1 and $3.20.
Is GLM-5 or Llama 3.2 1B better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 60K.
How many benchmarks do GLM-5 and Llama 3.2 1B share?
18 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Llama 3.2 1B has 22.