mukunda1729/model-pricing-table

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.