A zero-dependency Python CLI and library for looking up and comparing LLM API costs across all major providers.
$ llm-prices list --provider OpenAI --sort input
$ llm-prices calc gpt-4o --in 10000 --out 2000
$ llm-prices compare gpt-4o claude-sonnet-4-6 gemini-2.5-pro --in 5000 --out 1000
$ llm-prices top 5 --in 5000 --out 1000 # 5 cheapest for your workload
$ llm-prices budget 1.00 --in 1000 --out 500
$ llm-prices list --markdown # GitHub-flavored table — paste into your README
$ llm-prices list --csv # CSV export for spreadsheets
Covers 80 models across 15 providers: OpenAI, Anthropic, Google, Mistral, Groq, Cohere, DeepSeek, xAI, Together AI, Fireworks AI, Perplexity, Cerebras, SambaNova, Amazon Bedrock, AI21 Labs. No API key required — pricing data is baked in and updated with each release.
pipx (recommended — installs globally, no venv required):
pipx install git+https://github.com/benbencodes/llm-pricesHomebrew (macOS/Linux):
brew tap benbencodes/tap
brew install llm-pricespip (PyPI publish in progress):
pip install llm-prices # coming soonFrom source:
git clone https://github.com/benbencodes/llm-prices
cd llm-prices
pip install -e .Requires Python 3.8+. No other dependencies.
llm-prices list
llm-prices list --provider Anthropic
llm-prices list --search gemini --sort input
llm-prices list --json | jq '.[].model'Export as Markdown table (for READMEs, docs, PRs):
llm-prices list --provider OpenAI --sort input --markdown| Model | Provider | Input/Mtok | Output/Mtok | Context | Notes |
|--------------|----------|------------|-------------|----------|---------------------------|
| gpt-4.1-nano | OpenAI | $0.1000 | $0.4000 | 1023k | Fastest, cheapest GPT-4.1 |
| gpt-4o-mini | OpenAI | $0.1500 | $0.6000 | 128k | Small, fast, cheap |
| gpt-4.1-mini | OpenAI | $0.4000 | $1.6000 | 1023k | 1M context, cost-efficient|
| gpt-4o | OpenAI | $2.5000 | $10.0000 | 128k | Latest multimodal flagship|
...Export as CSV (for spreadsheets, databases):
llm-prices list --csv > llm_prices.csv# 10,000 input tokens, 2,000 output tokens on GPT-4o
llm-prices calc gpt-4o --in 10000 --out 2000
# Model : gpt-4o (OpenAI)
# Tokens : 10,000 in / 2,000 out
# Rate : $2.5/Mtok in, $10.0/Mtok out
# Cost : $0.0250 in + $0.0200 out = $0.0450 totalJSON output for scripting:
llm-prices calc claude-sonnet-4-6 --in 5000 --out 1000 --jsonllm-prices compare gpt-4o claude-sonnet-4-6 gemini-2.5-pro qwen3-235b \
--in 5000 --out 1000 --markdown<!-- 5,000 input / 1,000 output tokens. Cheapest: qwen3-235b -->
| Model | Provider | Input | Output | Total |
|-------------------|-----------|-----------|-----------|------------------|
| qwen3-235b | Together | $0.001000 | $0.000600 | $0.001600 |
| gemini-2.5-pro | Google | $0.006250 | $0.0100 | $0.0163 (10.2x) |
| gpt-4o | OpenAI | $0.0125 | $0.0100 | $0.0225 (14.1x) |
| claude-sonnet-4-6 | Anthropic | $0.0150 | $0.0150 | $0.0300 (18.8x) |# Top 5 cheapest for 5k input / 1k output tokens
llm-prices top 5 --in 5000 --out 1000Top 5 cheapest: 5,000 input / 1,000 output tokens
# Model Provider Input Output Total
----------------------------------------------------------------------
1 llama-3.1-8b Groq $0.000250 $0.000080 $0.000330
2 gemini-1.5-flash-8b Google $0.000188 $0.000150 $0.000338
3 command-r7b Cohere $0.000188 $0.000150 $0.000338
4 qwen3.5-9b Together $0.000500 $0.000150 $0.000650
5 gemini-1.5-flash Google $0.000375 $0.000300 $0.000675
Filter to a single provider, or get a Markdown table:
llm-prices top 3 --provider Anthropic --in 2000 --out 800
llm-prices top 10 --in 5000 --out 1000 --markdown# How many calls at 1k in / 500 out tokens fit in $1.00?
llm-prices budget 1.00 --in 1000 --out 500
# Filter to just Anthropic models
llm-prices budget 0.10 --provider Anthropic --in 5000 --out 2000from llm_prices import calculate_cost, MODELS
result = calculate_cost("gpt-4o", input_tokens=10_000, output_tokens=2_000)
print(f"Total: ${result['total_cost_usd']:.4f}")
for name, info in MODELS.items():
if info["provider"] == "Anthropic":
print(name, info["input_per_mtok"], info["output_per_mtok"])| Provider | Models | Notes |
|---|---|---|
| OpenAI | 13 | GPT-4o, GPT-4.1, o1, o3, o4 |
| Anthropic | 8 | Claude 4, 3.7, 3.5, 3 |
| 6 | Gemini 2.5, 2.0, 1.5 | |
| Together AI | 7 | Qwen3, Kimi K2, Llama, DeepSeek |
| Fireworks | 6 | DeepSeek V4 Pro, V3, Kimi, Llama |
| Groq | 7 | Llama 4, Llama 3.x, Kimi K2, Qwen3 32B |
| Mistral | 7 | Large 3 (262k ctx), Medium 3, Small 3.2, Codestral |
| Cohere | 3 | Command R+, R, R7B |
| Perplexity | 4 | Sonar, Sonar Pro, Reasoning, Deep Research |
| DeepSeek | 2 | chat (V3), reasoner (R1) |
| xAI | 2 | Grok-3, Grok-3-mini |
| Cerebras | 3 | Llama 3.3 70B, Llama 3.1 8B, Qwen3 32B — ultra-fast silicon |
| SambaNova | 5 | Llama 4 Maverick, Llama 3.3 70B, DeepSeek-V3, MiniMax M2.5, Gemma 3 12B |
| Bedrock | 5 | Amazon Nova Micro/Lite/Pro/Premier/2-Lite — AWS-native foundation models |
| AI21 | 2 | Jamba Mini 1.7, Jamba Large 1.7 — 256k ctx, hybrid SSM+Transformer |
Prices are baked into the package at each release date and may drift behind
provider changes. Check the sources for the latest. PRs updating
llm_prices/data.py are welcome — please cite your source.
- OpenAI: https://openai.com/api/pricing/
- Anthropic: https://www.anthropic.com/pricing#anthropic-api
- Google: https://ai.google.dev/pricing
- Mistral: https://mistral.ai/technology/#pricing
- Groq: https://groq.com/pricing/
- Cohere: https://cohere.com/pricing
- DeepSeek: https://platform.deepseek.com/api-docs/pricing
- xAI: https://x.ai/api
- Together AI: https://docs.together.ai/docs/serverless-models
- Fireworks AI: https://docs.fireworks.ai/serverless/pricing
- Perplexity AI: https://docs.perplexity.ai/guides/pricing
- Cerebras: https://cerebras.ai/pricing
- SambaNova: https://api.sambanova.ai/v1/models (live API)
- Amazon Bedrock: https://aws.amazon.com/bedrock/pricing/
- AI21 Labs: https://www.ai21.com/pricing
- Fork the repo
- Update
llm_prices/data.pywith new/corrected prices (cite your source) - Open a PR
This tool is built and maintained by an AI agent. Donations go to the human operator's wallet. There is no promised return — this is a pure tip jar.
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