Llama 3.3 70B Instruct vs GLM 4.5 Air
meta-llama/llama-3.3-70b-instruct · z-ai/glm-4.5-air
As of November 4, 2026, GLM 4.5 Air is the cheaper API on UberLLM: $0.0001 per million input tokens and $0.0008 per million output, versus $0.0145 / $0.0465 for Llama 3.3 70B Instruct, about 98% less when output is 3 tokens for every input token. These are the best prices on the market right now. Both work with one OpenAI-compatible key and UberLLM adds no fee. Choose GLM 4.5 Air to cut cost; choose Llama 3.3 70B Instruct when its capabilities fit your task better.
Llama 3.3 70B Instruct vs GLM 4.5 Air, side by side
| Spec | Llama 3.3 70B Instruct | GLM 4.5 Air |
|---|---|---|
| Price · input / 1M | $0.0145 | $0.0001 |
| Price · output / 1M | $0.0465 | $0.0008 |
| Context length | 128,000 | 128,000 |
| Max output tokens | 4,096 | 32,768 |
| Input modalities | text | text |
| Output modalities | text | text |
| Tools / functions | Yes | Yes |
| Reasoning | — | Yes |
| % off list | 92.7% off | 99.9% off |
Best prices on UberLLM right now. No UberLLM fee on requests; adding credit by card costs 5.5% + $0.50. Updated November 4, 2026. A price more than 70% below list is what buyers were billed, not an error. Why so cheap?
Llama 3.3 70B Instruct vs GLM 4.5 Air — FAQ
Is Llama 3.3 70B Instruct or GLM 4.5 Air cheaper?
GLM 4.5 Air is cheaper on a blended token mix, by about 98%. A request costs what its provider charged, UberLLM adds no fee, and every request is listed with its cost in Activity.
What's the price difference between Llama 3.3 70B Instruct and GLM 4.5 Air?
Llama 3.3 70B Instruct: $0.0145 in / $0.0465 out per 1M tokens. GLM 4.5 Air: $0.0001 in / $0.0008 out per 1M tokens. That's roughly a 98% blended difference. Prices are live; UberLLM adds no fee.
Can I use both Llama 3.3 70B Instruct and GLM 4.5 Air with one API?
Yes. Both are served through a single OpenAI-compatible endpoint — set the base URL to https://api.uberllm.dev/v1 and your UberLLM key, then switch between them by changing the model id. Anthropic SDKs work via https://api.uberllm.dev/v1/messages.