Text model
meta-llama/Llama-3.3-70B-Instruct-Turbo pricing
DeepInfra charges $0.10 per million input tokens and $0.32 per million output tokens for meta-llama/Llama-3.3-70B-Instruct-Turbo. Below: every rate that applies, what common workloads cost, and cheaper options.
- Input
- $0.10/ 1M
- Output
- $0.32/ 1M
- Context
- 131.1Ktokens
All rates
| Tier | Input | Output | Cache read | Cache write |
|---|---|---|---|---|
| Standard | $0.10 | $0.32 | — | — |
USD per million tokens. Long-context tiers apply when the whole prompt, cached tokens included, passes the threshold.
Work it out
What common workloads cost
| Workload | Per call | Cost per call | Calls a month | Per month |
|---|---|---|---|---|
| Chat reply | 1,500 input and 400 output tokens | $0.000278 | 100,000 | $27.80 |
| RAG answer, cached context | 2,000 fresh + 4,000 cached input, 500 output | $0.00076 | 100,000 | $76.00 |
| Agent step | 4,000 fresh + 8,000 cached input, 800 output | $0.00146 | 250,000 | $364.00 |
| Batch summary | 3,000 input and 300 output tokens via the Batch API | — | 1,000,000 | — |
Planning a mix of models? Simulate a workload with meta-llama/Llama-3.3-70B-Instruct-Turbo, alongside anything else you run.
Cheaper options
Current models that cost at least 15% less for a typical chat reply. Cheaper isn't always equivalent: test quality on your own prompts, then use veqen diff to see the impact at your volume.
| Model | Chat reply | Saving |
|---|---|---|
| deepinfra/Qwen/Qwen3.5-9B | $0.00021 | 24% |
| deepinfra/deepseek-ai/DeepSeek-V4-Flash | $0.000207 | 26% |
| openai/gpt-5-nano | $0.000235 | 15% |
| databricks/databricks-gpt-5-nano | $0.000235 | 15% |
Price history
No price changes since Veqen started tracking this model.
Compare
Last verified with price book 2026-10-22. Sources: DeepInfra pricing and LiteLLM's MIT-licensed price file. Track every call to this model, per customer or project: install the SDK.