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LLM API Cost Calculator: Know Your Bill Before It Arrives

Compare token pricing across GPT-4o, Claude, Gemini, and Mistral so you can pick the right model for your budget.

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LLM API pricing has a quirk that trips up most teams: output tokens cost 3-5x more than input tokens. A model priced at $3/million input tokens may charge $15/million output tokens. For applications that generate long completions — summaries, code, structured reports — the output side dominates the bill.

As of mid-2026, the major model prices are: GPT-4o at $2.50/million input, $10/million output. Claude 3.5 Sonnet at $3/million input, $15/million output. Gemini 1.5 Pro at $1.25/million input, $5/million output (under 128K context). Gemini 2.0 Flash at $0.075/million input, $0.30/million output — the lowest cost frontier model. Mistral Large 2 at $2/million input, $6/million output.

To estimate monthly spend: calculate average tokens per call (input + output), multiply by calls per day, multiply by 30, then apply the token price. A support bot making 10,000 calls/day with 500 input tokens and 300 output tokens per call: 10,000 × 30 = 300,000 calls/month. At Claude 3.5 Sonnet: 300,000 × 500 × ($3/1M) = $450 input + 300,000 × 300 × ($15/1M) = $1,350 output = $1,800/month. The same workload on Gemini 2.0 Flash: $22.50 input + $27 output = $49.50/month — a 36x difference.

Caching changes the math significantly. Anthropic's prompt caching (for repeated system prompts) costs $0.375/million cached reads instead of $3. If your system prompt is 2,000 tokens and every call reuses it, caching alone cuts input costs 75%.

Context length also affects price. Gemini 1.5 Pro charges $2.50/million input for prompts over 128K tokens. A model that looks cheap for short prompts may not be for long-context workloads like document analysis or codebase review.

The practical playbook: use a fast, cheap model (Gemini Flash, GPT-4o mini at $0.15/million input) for classification, routing, and simple generation. Reserve expensive models (GPT-4o, Claude 3.5 Sonnet) for tasks where quality demonstrably matters — complex reasoning, code generation, nuanced writing. Profile actual token usage in production before optimizing; developers consistently underestimate output token length.

Frequently Asked Questions

Why are output tokens more expensive than input tokens?

Generating each output token requires a full forward pass through the model, while input tokens are processed in parallel. The autoregressive generation process is compute-intensive per token, which is reflected in the pricing. Output tokens typically cost 3-5x input tokens across all major providers.

What is prompt caching and how much does it save?

Prompt caching stores repeated prefix tokens (like system prompts or large documents) in the model's KV cache. Anthropic charges $0.375/million for cached reads vs $3/million for fresh input — an 87.5% discount. OpenAI offers similar caching automatically for prompts over 1,024 tokens. For applications with fixed system prompts, caching is the single highest-ROI optimization.

Which LLM API is cheapest for code generation?

Gemini 2.0 Flash at $0.075/million input, $0.30/million output is the cheapest capable model as of 2026. For code quality, DeepSeek V3 (API via Fireworks, Together, or direct) offers strong coding performance at $0.27/million input, $1.10/million output. GPT-4o and Claude 3.5 Sonnet produce marginally better code but cost 10-40x more per token.

How do I reduce LLM API costs in production?

Four highest-ROI changes: enable prompt caching for repeated system prompts (75-87% discount on cached tokens), use a cheaper model for routing and classification before calling the expensive model, cap output token limits to prevent runaway generation, and batch requests where latency is not critical — batching typically unlocks 50% discounts on OpenAI and Anthropic.

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