Datasets:

mukunda1729
/

model-pricing-table

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.

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 and @mukundakatta/llm-cost-guard consume this table directly.

Part of The Agent Reliability Stack.

License

MIT.