Llama 3.3 70B Instruct vs GLM-4.7
meta-llama/llama-3.3-70b-instruct · z-ai/glm-4.7
As of October 31, 2026, Llama 3.3 70B Instruct is the cheaper API on UberLLM: $0.0479 per million input tokens and $0.1533 per million output, versus $0.1021 / $0.3742 for GLM-4.7, about 59% less when output is 3 tokens for every input token. These are the median prices buyers paid over the last 24 hours. Both work with one OpenAI-compatible key and UberLLM adds no fee. Choose Llama 3.3 70B Instruct to cut cost; choose GLM-4.7 when its capabilities fit your task better.
Llama 3.3 70B Instruct vs GLM-4.7, side by side
| Spec | Llama 3.3 70B Instruct | GLM-4.7 |
|---|---|---|
| Price · input / 1M | $0.0479 | $0.1021 |
| Price · output / 1M | $0.1533 | $0.3742 |
| 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 | 52% off | 81% off |
Median prices buyers paid on UberLLM over the last 24 hours. No UberLLM fee on requests; adding credit by card costs 5.5% + $0.50. Updated October 31, 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-4.7 — FAQ
Is Llama 3.3 70B Instruct or GLM-4.7 cheaper?
Llama 3.3 70B Instruct is cheaper on a blended token mix, by about 59%. 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-4.7?
Llama 3.3 70B Instruct: $0.0479 in / $0.1533 out per 1M tokens. GLM-4.7: $0.1021 in / $0.3742 out per 1M tokens. That's roughly a 59% blended difference. Prices are live; UberLLM adds no fee.
Can I use both Llama 3.3 70B Instruct and GLM-4.7 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.