LLM Token Cost Calculator & Multi-Model Estimator
Estimate input and output token costs across OpenAI, Anthropic, Google, DeepSeek, xAI, Mistral and Meta models. Compare per-request pricing, project volume costs, and export a CSV — entirely in your browser.
1 · Prompt & Tokens
Token count is estimated client-side. Actual counts vary by model tokenizer.
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Est. tokens
Only charged on reasoning models. Priced as output tokens.
2 · Select Models
Prices per 1M tokens. Click a model to select. Pin favorites to keep them at the top.
Cost Comparison Results
How to estimate LLM API costs before you ship
The fastest way to blow a budget on an AI feature is to ship first and measure cost later. Every production LLM integration should start with a back-of-the-envelope estimate: input tokens × input price + output tokens × output price × volume. This calculator does that math across 25+ models from OpenAI, Anthropic, Google, DeepSeek, xAI, Mistral, and Meta — so you can compare apples to apples before writing a single line of API code.
Start by picking a preset that matches your use case (chat, summarization, code generation, translation, RAG, long-document), then tune the exact token counts. Select the models you're considering, and the table will rank them cheapest-first with a visual cost bar and a clear Best Value badge.
LLM pricing comparison (per 1M tokens)
Snapshot of headline rates across major providers as of September 2026:
| Model | Provider | Input / 1M | Output / 1M | Context |
|---|---|---|---|---|
| GPT-4o | OpenAI | $2.50 | $10.00 | 128K |
| GPT-4o mini | OpenAI | $0.15 | $0.60 | 128K |
| Claude Sonnet 4 | Anthropic | $3.00 | $15.00 | 200K |
| Claude 3.5 Haiku | Anthropic | $0.80 | $4.00 | 200K |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | |
| DeepSeek V3 | DeepSeek | $0.27 | $1.10 | 128K |
| DeepSeek R1 | DeepSeek | $0.55 | $2.19 | 128K |
| Llama 4 Scout | Meta (API) | $0.17 | $0.66 | 10M |
The three hidden cost multipliers most teams miss
- Output tokens dominate cost. On frontier models like Claude Opus 4 or GPT-4o, the output price is 4–5× the input price. A 500-token response can cost more than a 5,000-token prompt.
- Reasoning tokens add up silently. Models like o3, o4-mini, DeepSeek R1, and Claude-with-thinking produce internal "reasoning" tokens that are billed as output. A simple math question can spawn 2,000+ hidden tokens.
- System prompts repeat on every call. A 2,000-token system prompt sent with every request in a chat app adds millions of billed tokens per month at scale. Use provider caching to discount them.
Batch API and prompt caching discounts
Most major providers offer a 50% discount on batch API calls — ideal when latency isn't critical (nightly summarization, analytics, report generation, backfills). Anthropic, OpenAI, and Google all offer prompt caching with similar 50–90% discounts on repeated input tokens, which can collapse the cost of long system prompts or RAG contexts.
Toggle "Batch API" and "Cached input" in the calculator to see what those discounts do to your volume bill.
How this estimator works
Token estimation uses a blended heuristic — roughly 1 token per 4 characters, cross-checked against a word-based count, averaged to match typical English-language tokenizer behavior. It's accurate within 10–20% for most natural-language prompts. For code-heavy, non-English, or highly tokenized content, enter a known token count via the override field.
Pricing data is bundled in the page and updated manually. All calculations happen locally — no data is sent anywhere, no API keys are required, and the tool works offline once loaded.
Frequently asked questions
For plain English text, the estimate is typically within 10–20% of the actual tokenizer count. Code, math, non-Latin scripts, and highly repetitive content can deviate more. For precise budgeting, paste a sample prompt into the model's official playground and copy the exact token count into the override field.
Reasoning models like OpenAI's o3/o4-mini, DeepSeek R1, and Claude with extended thinking generate internal "thinking" tokens that aren't shown to the user but are billed at the output rate. A simple prompt can trigger thousands of hidden reasoning tokens. The "Thinking tokens" field lets you budget for this — set it to 0 for non-reasoning models.
Batch APIs let you submit many requests asynchronously (usually completed within 24 hours) in exchange for roughly 50% off standard pricing. OpenAI, Anthropic, and Google all offer this. Toggle "Batch API" in the calculator to see the discounted totals. Ideal for nightly jobs, analytics, and non-real-time workflows.
Prompt caching discounts vary: Anthropic offers 90% off on cached input tokens, OpenAI offers 50% on qualifying cached prefixes, and Google has similar mechanisms. The toggle in this calculator assumes a conservative 50% average. For exact savings, check each provider's docs and factor in the cache TTL and minimum prefix length.
Generating output requires running inference token-by-token with no opportunity to amortize compute across a batch. Input tokens can be pre-processed in parallel. That asymmetry is why output typically costs 3–5× input on frontier models. It's also why short, concise responses are the single biggest cost lever you have.
Pricing data was last updated September 2026. LLM prices drop frequently — sometimes 30–50% in a single announcement. Always verify current rates on official provider documentation before making budget or architecture decisions. The relative ranking between models tends to be more stable than absolute prices.
No. Every calculation — token estimation, cost math, sorting, CSV export — runs locally in your browser using JavaScript. Your prompts, token counts, and selected models never leave your device. The tool works fully offline once the page is loaded.