Llama-3.3-70B-Instruct
meta · meta/llama-3.3-70b-instruct
Popular open Llama workhorse for multilingual chat, coding, and self-hosting
≈85k Chinese characters of context · Calls tools / agents · Structured JSON output · Open weights, self-hostable
Read a 200,000-Chinese-character book and write a 2,000-Chinese-character summary: about No public rate
An order-of-magnitude estimate at 1.5 tokens per Chinese character and the vendor's own rate — not a quote. Full method: the glossary on the AI Models page.
Specs
| Context | 128,000(≈85k Chinese characters) |
|---|---|
| Max output | 4,096 |
| Structured output | Yes |
| Open weights | Yes |
| Released | 2024-12-06 |
Source: models.dev snapshot 2026-08-29
Who serves it, at what price
| Provider | In /M | Out /M | Cache read /M |
|---|---|---|---|
| Azure | $0.71 | $0.71 | — |
| Azure Cognitive Services | $0.71 | $0.71 | — |
| Cortecs | $0.129 | $0.399 | — |
| GreenPT | $1.254 | $1.254 | — |
| Helicone | $0.13 | $0.39 | — |
| Charm Hyper | $0.6066 | $1.0386 | — |
| Llama | Billed by plan | Billed by plan | — |
| DevPass (LLM Gateway) | $0.13 | $0.4 | — |
| Regolo AI | $0.6 | $2.7 | — |
| Scaleway | $0.9 | $0.9 | — |
Rows tagged “first-party” are the model vendor's own pricing; untagged rows are resale channels and may differ.
When to choose it
We have not assessed this model. It comes from the full models.dev snapshot — the specs are the vendor's published figures, and a price is the vendor's own only on rows tagged first-party; the rest are resale channels. The fit is simply not something anyone here has written.
Leaderboard
Not yet on any leaderboard we track. That does not mean the model performs poorly — it means we have not taken this snapshot for it yet, and we do not publish a score we have not measured.