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license: mit
language:
- en
tags:
- llm
- pricing
- cost
- openai
- anthropic
- google
- meta
- reference
size_categories:
- n<1K
pretty_name: LLM Pricing Table
configs:
- config_name: default
data_files:
- split: train
path: data.jsonl
---
# LLM Pricing Table
Per-1k-token input/output costs for the major LLM models, in a single loadable JSONL. Useful for cost-estimator dashboards, budget enforcement, and ROI analysis.
```python
from datasets import load_dataset
ds = load_dataset("mukunda1729/model-pricing-table", split="train")
prices = {row["model"]: row for row in ds}
print(prices["claude-sonnet-4-6"]["input_per_1k_tokens_usd"]) # 0.003
```
## Schema
| Field | Type | Notes |
|---|---|---|
| `model` | `str` | Canonical model identifier |
| `provider` | `str` | `openai` / `anthropic` / `google` / `meta` / `mistral` / `deepseek` |
| `input_per_1k_tokens_usd` | `float` | USD per 1,000 input tokens |
| `output_per_1k_tokens_usd` | `float` | USD per 1,000 output tokens |
| `context_window` | `int` | Max tokens (input + output) |
| `modality` | `str` | `text` / `multimodal` |
## Data freshness
Snapshot as of 2026-04-27. Provider prices change — always cross-reference the official pricing page before relying on these in production billing.
Sister tooling: [`llm-cost-guard-py`](https://pypi.org/project/llm-cost-guard-py/) and [`@mukundakatta/llm-cost-guard`](https://www.npmjs.com/package/@mukundakatta/llm-cost-guard) consume this table directly.
Part of [The Agent Reliability Stack](https://mukundakatta.github.io/agent-stack/).
## License
MIT.
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