GLM-5.2 vs Llama 4 (large)
A side-by-side on the things that actually decide it: price, context window, size, and whether you can own the weights. Numbers are approximate and editable in the token counter.
| GLM-5.2 | Llama 4 (large) | |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | open | open |
| License | MIT | Llama license |
| Parameters | 744B (40B active) | 400B (17B active) |
| Context window | 1M | 256K |
| $ / M input | $1.20 | $0.50 |
| $ / M output | $4.10 | $1.60 |
| Sample task cost* | $0.0077 | $0.0031 |
*Sample task = 3,000 input + 1,000 output tokens. Approximate public figures as of mid-2026; prices change often. Verify live provider pricing before relying on these numbers.
The short answer
- Llama 4 (large) is cheaper on a sample task (about 2.5x).
- GLM-5.2 has the larger context window (1M).
- Both are open-weights, so either can be self-hosted and fine-tuned.
How to choose between them
Per-token price is the headline, but the honest unit is cost per finished task, since a chattier model can burn more tokens to do the same job. Run your real prompt through the token counter and your real loop through the agent cost simulator before committing. And weigh the column that compounds: open weights let you self-host, fine-tune, and pin a version, which is why a model like GLM-5.2 can matter beyond its sticker price.