Model comparison
GLM-5.3-Flash vs Llama 3.2 1B
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.4× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. GLM-5.3-Flash 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-5.3-Flash leads 58.4 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash 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 $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 60K.
Side by side
| GLM-5.3-Flash | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.8 | 20.1 |
| Released | 2026-08-20 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 1M | 60K |
| Max output | 131K | 54K |
| Input $ / M tokens | $0.15 | $0.027 |
| Output $ / M tokens | $0.50 | $0.20 |
| Results tracked | 40 | 22 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1508 | 1070 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1491 | 1044 |
| Epoch Capabilities Index | 151.88 | 101.99 |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 0.6% |
| LMArena Math | 1500 | 1086 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 90.2% | 23.9% |
| LMArena Expert | 1513 | 1007 |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Llama 3.2 1B: —
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1462 | 973 |
| LMArena Chinese | 1527 | 959 |
| LMArena German | 1470 | 1014 |
| LMArena Russian | 1469 | 941 |
| LMArena French | 1496 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1478 | 1031 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1482 | 1050 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GLM-5.3-Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1471 | 1055 |
| LMArena Creative Writing | 1442 | 1033 |
| LMArena Multi-Turn | 1467 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
Frequently asked questions
Is GLM-5.3-Flash better than Llama 3.2 1B?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.4× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash 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-5.3-Flash lists at $0.15 and $0.50.
Is GLM-5.3-Flash or Llama 3.2 1B better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 60K.
How many benchmarks do GLM-5.3-Flash and Llama 3.2 1B share?
17 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Llama 3.2 1B has 22.