Llama 3.2 1B Instruct
Pricing updated daily · Last refreshed Oct 29, 2026Meta model with a 60K-token context window, priced at $0.027 per million input tokens and $0.201 per million output tokens.
At a glance
- Provider
- Meta
- Context window
- 60,000 tokens
- Max output
- 54,000 tokens
- Input price
- $0.027 / 1M tokens
- Output price
- $0.201 / 1M tokens
- Input types
- text
- Function calling
- No
- Released
- September 25, 2024
What it costs
Illustrative request sizes, not measurements of any real product. For your own volumes, use the cost estimator.
| Workload | Tokens in / out | Per request | Per 1,000 requests |
|---|---|---|---|
| Short chat reply | 1,000 / 500 | $0.000128 | $0.1275 |
| Question over retrieved documents | 8,000 / 500 | $0.000316 | $0.3165 |
| Summarise a long document | 50,000 / 1,500 | $0.0017 | $1.65 |
How it compares
- Input price ($0.027 per 1M tokens) is the lowest of 30 models in the catalogue.
- Output price ($0.201 per 1M tokens) is the 4th lowest of 30.
- Context window (60K tokens) is the 30th largest of 30.
- Maximum output (54K tokens) is the 22nd largest of 30.
- On blended price it is 55% cheaper than the median Meta model in the catalogue.
These are rankings by listed price and size within this catalogue (blended price weights input 3:1 against output), not a measure of output quality.
Cheaper models with the same capabilities
Lower blended price, at least the same context window, and the same input types.
| Model | Provider | Context | Input $/1M | Output $/1M |
|---|---|---|---|---|
| Command R7B (12-2024) | Cohere | 128K | $0.037 | $0.15 |
More from Meta
| Model | Provider | Context | Input $/1M | Output $/1M |
|---|---|---|---|---|
| Llama Guard 4 12B | Meta | 164K | $0.18 | $0.18 |
| Llama 4 Maverick | Meta | 524K | $0.188 | $0.652 |
| Llama 4 Scout | Meta | 1.31M | $0.10 | $0.30 |
| Llama 3.3 70B Instruct | Meta | 131K | $0.10 | $0.32 |
Try it with your own text
- Token counter: see how many tokens a prompt uses (exact for OpenAI models, labelled estimates for others).
- Context window visualizer: see how much of this model’s 60K window a prompt fills.
- Model comparison: sort and filter every model, and export to CSV.
Source: OpenRouter’s public model catalogue (meta-llama/llama-3.2-1b-instruct), read once a day. These are the prices OpenRouter charges; a provider’s own list price can differ, so check the provider before committing to a budget.