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Calculate, compare, and optimize token costs and API pricing across OpenAI, Anthropic Claude, DeepSeek, Google Gemini, and custom AI agent loops with 100% browser privacy.
State-of-the-art hybrid reasoning & coding agent model.
Claude, DeepSeek, and OpenAI offer discounted rates (up to 90% off) for cached prompt prefixes.
Choose your target model (Claude 3.7 Sonnet, GPT-4o, DeepSeek R1, Gemini 1.5 Pro) and enter system prompt token overhead (`CLAUDE.md`, guidelines, schemas).
Configure execution turns per run, input/output token estimates per turn, prompt caching hit rates (up to 90% discount), and batch API discounts.
Compare exact costs per run, 1,000 runs, and monthly projections across all leading providers simultaneously, then copy TypeScript/Python logging code.
Zero API calls or backend data transmission. All calculations, token inputs, and pricing metrics execute locally in your browser.
Includes official token rates for OpenAI (GPT-4o, o1, o3-mini), Anthropic (Claude 3.7 Sonnet, 3.5 Haiku, 3 Opus), DeepSeek (V3, R1), Google Gemini (1.5 Pro, Flash), and Groq/Llama 3.3.
Simulates multi-turn agent workflows, context window accumulation per loop step, system prompt persistence, and tool response payload inflation.
Calculates cost savings for Anthropic, OpenAI, and DeepSeek prompt caching (read/write rates) and 50% batch API processing discounts.
Compares exact cost per single run, 1,000 runs, and estimated monthly production volume across all leading model providers simultaneously.
Generates ready-to-use Python and Node.js wrapper code to log token usage headers and track production LLM cost metrics dynamically.
As engineering teams transition from single-prompt LLM calls to autonomous agentic architectures (such as Claude Code CLI, Cursor agent mode, AutoGen, or LangGraph), total token consumption is no longer linear. In multi-turn execution loops, every subsequent step resends previous dialogue history, tool definition JSON schemas, and execution outputs back to the model context window.
Without prompt caching or context compression, an 8-turn coding agent loop can consume over 100,000 cumulative input tokens per user request. Implementing Anthropic or DeepSeek Prompt Caching or OpenAI Cached Inputs reduces input token billing by up to 90%, making production agent deployment economically viable at scale.
HiMat's AI Agent & LLM Token Cost Calculator operates 100% locally inside your web browser. Neither your custom prompt token counts, system guidelines, cost budgets, nor secret API credentials leave your local device.
The calculator takes your expected input tokens, output tokens, prompt caching usage, batch processing preferences, and agent loop execution turns, then multiplies them by official per-million-token API rates across OpenAI, Anthropic, DeepSeek, Google Gemini, and open-source hosting providers.
In agentic execution, each turn passes previous dialogue, tool output, and system instructions back to the model. The calculator accounts for context compounding per step: Input Tokens = System Prompt + Initial User Prompt + Sum of previous turns' responses and tool outputs.
Prompt Caching allows LLM providers to cache static prefixes (like system instructions or repository context). Anthropic Claude and DeepSeek offer up to 90% discount on cached input tokens, while OpenAI offers 50% discount on cached inputs, significantly lowering agent operational costs.
No. The HiMat AI Agent & LLM Token Cost Calculator runs 100% client-side in browser memory. No prompt text, token estimates, or pricing variables are sent to HiMat or any third party.
The calculator supports current official pricing for OpenAI (GPT-4o, GPT-4o-mini, o1, o3-mini), Anthropic (Claude 3.7 Sonnet, Claude 3.5 Haiku, Claude 3 Opus), DeepSeek (DeepSeek V3, DeepSeek R1), Google Gemini (Gemini 1.5 Pro, Gemini 1.5 Flash), and Meta Llama 3.3 70B.
HiMat Technologies engineers context-efficient agentic pipelines, semantic caching layers, custom MCP integrations, model routing architectures, and 21-day fixed-scope AI pilots with complete source code ownership.
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