Free LLM API Pricing Tool
AI Token Cost Calculator
Instantly calculate and compare API token costs across OpenAI, Claude, Gemini, DeepSeek, Grok and more — before you commit a single dollar to your LLM budget.
23+
AI Models
6
Providers
3
Calc Modes
100%
Free Forever
✦ OpenAI GPT-4o / GPT-4.1
✦ Claude Sonnet / Opus
✦ Gemini 2.5 Pro
✦ DeepSeek V3
✦ Grok 3
✦ Mistral
Token & Cost Estimator
📝 Text Input
🔢 Manual Token Count
📊 Monthly Usage
Characters: 0
Words: 0
Est. Input Tokens: 0
% of input tokens expected as output
💡 We'll project your costs per day, week, and month across all selected models.
📊 Cost Breakdown Across All Providers
Input Tokens
–
Output Tokens
–
Total Tokens
–
API Calls
–
Cheapest Option
–
Cheapest Cost
–
Sort by:
| Provider / Model | Input Cost | Cached Input | Output Cost | Total (× Calls) |
|---|
How to Use the Token Cost Calculator
1
Choose Your Input Method
Paste your actual prompt text into the Text Input tab for an automatic token count — or switch to Manual Token Count if you already know your token figures from a previous API call.
2
Set Output Ratio & Call Volume
Adjust the output token ratio slider to reflect how verbose the model's response typically is. Enter the number of API calls you expect to make (daily, weekly, or in a single batch).
3
Filter Providers
Toggle the provider checkboxes to focus on the APIs you're actually evaluating — whether that's OpenAI vs Claude head-to-head, or a full LLM market comparison.
4
Hit Calculate & Review
Click "Calculate Token Costs" to see a live breakdown across all selected models — including input cost, output cost, cached token savings, and a total per-call and aggregate figure.
5
Project Monthly Spend
Switch to the Monthly Usage tab, enter your daily call volume and average token sizes, then calculate to see projected daily, weekly, and monthly costs — perfect for budget planning.
Why Token Pricing Deserves More Attention Than You're Giving It
Frequently Asked Questions
How does the token cost calculator estimate token count from text?
Token count is estimated using a standard approximation of 4 characters per token — the widely accepted heuristic used by OpenAI, Anthropic, and Google for English-language text. This aligns with the cl100k_base tokenizer used by GPT-4 and GPT-3.5 models. For precise token counts, especially for non-English languages or code-heavy prompts, you should use the model provider's native tokenizer (such as OpenAI's tiktoken library). This calculator gives you a fast, reliable estimate suitable for budget planning and cost comparisons across providers.
What is the difference between input tokens, output tokens, and cached tokens?
Input tokens are the tokens in the prompt you send to the model — including your system message, user message, conversation history, and any documents you pass in as context. Output tokens are the tokens the model generates in its response. Output tokens are typically billed at a higher rate because they require active computation (autoregressive generation), while input tokens are processed in a single forward pass. Cached tokens (available on Claude via prompt caching, and on Gemini via context caching) are input tokens retrieved from a server-side cache rather than reprocessed. They're billed at a significant discount — often 80–90% less than standard input token rates — making them highly valuable for applications with consistent large system prompts or repeated context.
Which LLM model is the cheapest for API use in 2025?
For raw cost per token, DeepSeek V3 and Mistral's smaller models consistently sit at the bottom of the price table. GPT-4o-mini and Claude Haiku 3.5 offer a strong balance of capability and cost for production workloads that don't need top-tier reasoning. If you need full frontier-model intelligence, the cost-per-token ranking as of mid-2025 typically runs: DeepSeek V3 < Gemini Flash < GPT-4o-mini / Claude Haiku < GPT-4.1 / Gemini 2.5 Pro < Claude Sonnet < Claude Opus / GPT-4o. The right choice depends on your quality threshold — a cheaper model running at 100% accuracy on your task is always the right choice over a premium model running at 102% accuracy at 10× the cost.
How do I calculate OpenAI API costs for a production application?
Start by profiling a representative sample of real calls — log the input token count, output token count, and completion latency across at least 100–200 production-like requests. This gives you an accurate average rather than a theoretical estimate. Then multiply: (avg input tokens × input price per token) + (avg output tokens × output price per token) = cost per call. Multiply by your expected daily call volume to get daily spend, then ×30 for monthly. Factor in batch API discounts if your workload is asynchronous (50% off), and cached input tokens if your system prompt is large and consistent (up to 50% off on OpenAI). This calculator handles all of that arithmetic automatically — just feed it your profiled averages.
Does this token calculator support image token costs?
This calculator focuses on text token costs across all major LLM providers. Image tokens (used in vision-capable models like GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro) are calculated differently by each provider. OpenAI uses a tile-based system where image cost depends on resolution and detail level — a high-detail 1024×1024 image costs roughly 765 tokens. Anthropic calculates image tokens based on pixel dimensions: tokens = (width × height) / 750. For multimodal workloads with significant image volume, we recommend using the provider's native image token calculator alongside this tool's text cost estimates to get a combined figure.