LLM Cost Comparison — 12+ Models Side by Side

Compare API costs across 12+ LLMs: GPT-4o, Claude, Gemini, Llama, Mistral. Same workload, real dollar differences. Free token calculator, no signup.

Model choice is a cost decision as much as a quality decision. Enter your workload once — a sample prompt and response — and see what the identical traffic would cost across GPT-4o, Claude Sonnet and Haiku, Gemini Flash and Pro, Llama, and Mistral variants.

The comparison splits input and output pricing, includes cached-input rates where relevant, and projects monthly spend at your request volume. For many workloads the spread between the most and least expensive competent model is more than 10×.

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Developer/LLM Token & Cost Calculator

LLM Token & Cost Calculator

Estimate token counts and API costs for OpenAI, Anthropic, Google, Meta & DeepSeek models.

18 MODELS10 modelsAPI pricingToken estimateContext usage
0 chars · 0 words
08K16K32K
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Paste text and select models to see token estimates
Data Source & Legal Disclaimer
Effective: Pricing as of August 2026 (subject to change)Last updated: 2 weeks agoUpdate: Manual review

LLM API prices are per 1M tokens as of June 2026 and may change without notice. Token estimates use model-specific chars-per-token ratios and may vary ±10-20% from actual counts.

How tokenizers chop text and price API calls — illustrated

LLMs don't read characters — they read tokens. A tokenizer (BPE or SentencePiece, e.g. GPT's cl100k_base/o200k_base, Claude's tokenizer, Gemini's SentencePiece) splits text into subword units, and English averages roughly 4 characters per token (CJK and dense code can differ). This tool estimates input tokens as ceil(chars ÷ charsPerToken) for each model, then computes cost as tokens ÷ 1,000,000 × per-1M price for input and output and sums them. It also shows what fraction of the model's context window the call would consume.

Text → subword tokens → tokens ÷ 1M × price
The quick brown fox jumps over the lazy dogThequickbrownfox+ 5 more≈ 9 tokens · English averages ≈ 4 chars/tokencost = (tokens ÷ 1M) × price-per-1Minput cost + output cost, summedMODELSgpt-4oBPE · o200k_baseclaude-4-sonnetClaude tokenizergemini-2.5-proSentencePieceeach: own c/t + $/1Mcontext % = (input + output) ÷ context window × 100

A token is often a whole common word, or a subword chunk of a longer word. Because each model ships its own tokenizer and its own per-1M prices, the same prompt yields slightly different token counts and costs across providers.

Pricing a prompt before you ship the API call

Ken, an app developer, is about to call an LLM endpoint with a 1,024-character prompt and wants to know which model is cheapest before the invoice lands.

  1. Paste the prompt:1,024 chars ÷ ~4 chars/token ≈ 256 input tokens (English text)
  2. Set output budget:500 output tokens via the slider — the completion allowance
  3. Compare models:Rows sort by total cost; input and output priced separately per model
  4. Watch context %:(input + output) ÷ context window × 100 — flagged red past 80%

About this LLM cost comparison

This page covers compare llm pricing, claude vs gpt-4o cost, gemini flash vs gpt-4o mini price, cheapest llm api — all the same underlying task as LLM cost comparison. The tool above is FreeToolHub's token calculator embedded in full: every feature works right here, and nothing you process is uploaded to any server.

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Frequently asked questions

Which LLM gives the best cost-to-quality ratio?

It depends on the task: classification, extraction, and routing usually run fine on small models (GPT-4o mini, Claude Haiku, Gemini Flash) at 5–10% of flagship cost. Complex reasoning and long-context analysis justify larger models. Run your real workload through the calculator to see the dollar difference before deciding.

Do token prices differ between input and output?

Yes — output tokens typically cost 3–5× more than input tokens across all major providers. A workload that generates long responses is dominated by output pricing, while RAG applications with large context windows are dominated by input. The comparison accounts for both separately.

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