LLM Cost Calculator
Calculate and compare LLM API costs for GPT-4, Claude, Gemini, and other AI models. Estimate per-request pricing and monthly costs based on your usage. This free calculator includes pricing for OpenAI, Anthropic, Google, and Meta models with instant cost comparisons across providers.
Select model
Choose your AI model (GPT-4, Claude, Gemini, etc.).
Estimate tokens
Enter input/output tokens per request and monthly volume.
Compare costs
View per-request and monthly costs across all models.
Calculate LLM API Costs
Cost Comparison Across Models
| Model | Per Request | Monthly Cost | Input Rate | Output Rate |
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How to Calculate LLM Costs
LLM API costs are calculated based on token usage. Most providers charge separately for input (prompt) and output (completion) tokens. Share your cost optimization strategies with 500+ AI builders implementing production workflows.
Pricing is typically per 1 million tokens. For example, if GPT-4o charges $2.50 per 1M input tokens, 1,000 input tokens cost $0.0025.
Understanding LLM Pricing
What are tokens in LLM pricing?
Tokens are chunks of text that LLMs process. One token equals roughly 4 characters or 0.75 words in English. "Hello world" is 2 tokens. "The quick brown fox jumps over the lazy dog" is 9 tokens. Input tokens are your prompt, output tokens are the AI's response. Most models charge different rates for input vs. output tokens. Validate your LLM outputs using our JSON Schema Validator to reduce wasted tokens from malformed responses.
How do I estimate token usage?
Token estimation strategies:
- Rule of thumb: 1 token ≈ 4 characters or 0.75 words
- Exact counting: Use OpenAI's tiktoken library or provider tokenizers
- Test your prompts: Run 10-20 sample requests and average the token counts
- Track in production: Most APIs return token usage in response headers
- Add buffer: Estimate 20% higher than average to account for variability
Which model is most cost-effective?
Cost-effectiveness depends on your use case:
- Simple tasks (summaries, classifications): GPT-4o Mini, Claude Haiku, Gemini Flash (10-20x cheaper)
- Complex reasoning (analysis, coding): GPT-4o, Claude 3.5 Sonnet (balance of cost and quality)
- Maximum quality (research, creative): GPT-4 Turbo, Claude Opus (highest cost, best performance)
- High-volume automation: Llama 3.1 (self-hosted or cheapest API pricing)
Always test quality before optimizing for cost. A cheaper model that requires 3x more requests costs more than a premium model that works first try.
Hidden costs to consider
Beyond per-token pricing:
- Rate limits: Hitting rate limits forces you to buy higher tiers or slow down processing
- Failed requests: Errors, timeouts, and retries add 5-15% to real costs
- Context window costs: Larger contexts (passing full documents) increase input token costs significantly
- Prompt engineering time: Hours spent optimizing prompts to reduce tokens
- Quality control: Human review of LLM outputs (often 10-30% of outputs need revision)
How can I reduce LLM costs?
Cost optimization tactics:
- Use smaller models: Route simple tasks to GPT-4o Mini or Claude Haiku (saves 80-90%)
- Compress prompts: Remove unnecessary context, examples, and whitespace
- Cache results: Store responses for identical or similar inputs
- Limit output tokens: Set max_tokens parameter to prevent runaway generation
- Batch requests: Combine multiple tasks in one API call when possible
- Use streaming: Stop generation early if output is sufficient
- Self-host open models: Llama, Mistral for high-volume predictable workloads
Calculate total savings including implementation costs with our AI ROI Calculator to determine if optimization efforts justify the engineering time required.