Text model
meta.llama4-maverick-17b-instruct-v1:0 pricing
Amazon Bedrock charges $0.24 per million input tokens and $0.97 per million output tokens for meta.llama4-maverick-17b-instruct-v1:0. Below: every rate that applies, what common workloads cost, and cheaper options.
- Input
- $0.24/ 1M
- Output
- $0.97/ 1M
- Context
- 1Mtokens
Retires 2027-03-30. npx @veqen/sdk scan finds every place your code uses it.
All rates
| Tier | Input | Output | Cache read | Cache write |
|---|---|---|---|---|
| Standard | $0.24 | $0.97 | — | — |
| Batch | $0.12 | $0.485 | — | — |
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 | $0.000748 | 100,000 | $74.80 |
| RAG answer, cached context | 2,000 fresh + 4,000 cached input, 500 output | $0.00193 | 100,000 | $192.50 |
| Agent step | 4,000 fresh + 8,000 cached input, 800 output | $0.00366 | 250,000 | $914.00 |
| Batch summary | 3,000 input and 300 output tokens via the Batch API | $0.000505 | 1,000,000 | $505.50 |
Planning a mix of models? Simulate a workload with meta.llama4-maverick-17b-instruct-v1:0, 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 |
|---|---|---|
| bedrock/openai.gpt-oss-120b-1:0 | $0.000558 | 25% |
| bedrock/nvidia.nemotron-super-3-120b | $0.000485 | 35% |
| deepinfra/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | $0.00062 | 17% |
| azure-ai/Llama-4-Scout-17B-16E-Instruct | $0.000612 | 18% |
Price history
No price changes since Veqen started tracking this model.
Compare
Last verified with price book 2026-10-22. Sources: Amazon Bedrock pricing and LiteLLM's MIT-licensed price file. Track every call to this model, per customer or project: install the SDK.