Model comparison
GLM-5V-Turbo vs Llama 3.2 1B
GLM-5V-Turbo is the stronger model overall, scoring 43.1 to 19.9 on the Noometry Index. Llama 3.2 1B costs 27× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
MetaSummary
- They share 13 benchmarks with published results for both. GLM-5V-Turbo scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5V-Turbo leads 62.6 to 21.4.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- GLM-5V-Turbo accepts more context: 200K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GLM-5V-Turbo | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 43.1 | 19.9 |
| Released | 2026-04-01 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 200K | 60K |
| Max output | 131K | 54K |
| Input $ / M tokens | $1.20 | $0.027 |
| Output $ / M tokens | $4 | $0.20 |
| Results tracked | 19 | 22 |
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Category by category
Coding GLM-5V-Turbo leads
GLM-5V-Turbo: 41.8 (#105), Llama 3.2 1B: 20.6 (#333)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1467 | 1071 |
| LMArena WebDev | 1400 | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
Agentic & Tool Use Not comparable
GLM-5V-Turbo: —, Llama 3.2 1B: 14.5 (#142)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
Reasoning GLM-5V-Turbo leads
GLM-5V-Turbo: 28.5 (#87), Llama 3.2 1B: 15.6 (#304)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1044 |
| Chess Puzzles | — | 0% |
| Epoch Capabilities Index | — | 101.95 |
Math GLM-5V-Turbo leads
GLM-5V-Turbo: 36.6 (#109), Llama 3.2 1B: 9.7 (#308)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Math | 1440 | 1086 |
| OTIS Mock AIME 2024-2025 | — | 0.6% |
Knowledge GLM-5V-Turbo leads
GLM-5V-Turbo: 39.9 (#113), Llama 3.2 1B: 7.2 (#307)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Expert | 1451 | 1007 |
| GPQA Diamond | — | 23.9% |
Multimodal Not comparable
GLM-5V-Turbo: 40.1 (#41), Llama 3.2 1B: —
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1264 | — |
| LMArena Document | 1416 | — |
Multilingual GLM-5V-Turbo leads
GLM-5V-Turbo: 53.2 (#69), Llama 3.2 1B: 23.9 (#287)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1420 | 973 |
| LMArena Chinese | 1488 | 958 |
| LMArena German | 1422 | 1014 |
| LMArena Russian | 1432 | 941 |
| LMArena French | 1443 | — |
| LMArena Korean | 1399 | — |
| LMArena Spanish | 1446 | — |
Instruction Following GLM-5V-Turbo leads
GLM-5V-Turbo: 75.1 (#75), Llama 3.2 1B: 52.6 (#285)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1422 | 1031 |
Long Context GLM-5V-Turbo leads
GLM-5V-Turbo: 44.0 (#76), Llama 3.2 1B: 31.9 (#269)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1439 | 1050 |
Writing & Preference GLM-5V-Turbo leads
GLM-5V-Turbo: 62.6 (#69), Llama 3.2 1B: 21.4 (#305)
| Benchmark | GLM-5V-Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1437 | 1055 |
| LMArena Creative Writing | 1414 | 1033 |
| LMArena Multi-Turn | 1432 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
Frequently asked questions
Is GLM-5V-Turbo better than Llama 3.2 1B?
GLM-5V-Turbo is the stronger model overall, scoring 43.1 to 19.9 on the Noometry Index. Llama 3.2 1B costs 27× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Which is cheaper, GLM-5V-Turbo 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-5V-Turbo lists at $1.20 and $4.
Is GLM-5V-Turbo or Llama 3.2 1B better for coding?
GLM-5V-Turbo scores higher on coding benchmarks: 41.8 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5V-Turbo does, with 200K tokens against 60K.
How many benchmarks do GLM-5V-Turbo and Llama 3.2 1B share?
13 benchmarks have published results for both models. GLM-5V-Turbo has 19 scored results on Noometry and Llama 3.2 1B has 22.