model stringlengths 2 17 | provider stringclasses 6
values | input_per_1k_tokens_usd float64 0 0.02 | output_per_1k_tokens_usd float64 0 0.08 | context_window int64 64k 2M | modality stringclasses 2
values |
|---|---|---|---|---|---|
gpt-5 | openai | 0.005 | 0.015 | 200,000 | text |
gpt-5-mini | openai | 0.00015 | 0.0006 | 128,000 | text |
gpt-4o | openai | 0.0025 | 0.01 | 128,000 | multimodal |
gpt-4o-mini | openai | 0.00015 | 0.0006 | 128,000 | multimodal |
o1 | openai | 0.015 | 0.06 | 200,000 | text |
o3-mini | openai | 0.0011 | 0.0044 | 200,000 | text |
claude-opus-4-1 | anthropic | 0.015 | 0.075 | 200,000 | multimodal |
claude-sonnet-4-6 | anthropic | 0.003 | 0.015 | 200,000 | multimodal |
claude-haiku-4-5 | anthropic | 0.0008 | 0.004 | 200,000 | multimodal |
claude-3-5-sonnet | anthropic | 0.003 | 0.015 | 200,000 | multimodal |
claude-3-5-haiku | anthropic | 0.0008 | 0.004 | 200,000 | multimodal |
gemini-2-5-pro | google | 0.00125 | 0.01 | 1,000,000 | multimodal |
gemini-2-5-flash | google | 0.000075 | 0.0003 | 1,000,000 | multimodal |
gemini-1-5-pro | google | 0.00125 | 0.005 | 2,000,000 | multimodal |
gemini-1-5-flash | google | 0.000075 | 0.0003 | 1,000,000 | multimodal |
llama-3.1-405b | meta | 0.005 | 0.005 | 128,000 | text |
llama-3.1-70b | meta | 0.0009 | 0.0009 | 128,000 | text |
llama-3.1-8b | meta | 0.0002 | 0.0002 | 128,000 | text |
mistral-large | mistral | 0.002 | 0.006 | 128,000 | text |
deepseek-r1 | deepseek | 0.00055 | 0.00219 | 64,000 | text |
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.
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