Model APIs and token usage
Text-model costs can depend on input tokens, cached input, output tokens, request volume, model choice, and whether a provider offers a separate batch rate.
Estimate and compare costs across model APIs, token usage, caching, RAG, agents, media generation, evaluation, fine-tuning, and self-hosted inference.
Different AI workloads are billed in different ways. A useful estimate starts by matching the calculator to the part of the system that actually creates the cost.
Text-model costs can depend on input tokens, cached input, output tokens, request volume, model choice, and whether a provider offers a separate batch rate.
RAG and agent systems can add embeddings, vector storage, retrieval, reranking, web search, repeated model calls, retries, tools, memory, and human review to the base model cost.
Media workloads may be priced by image, clip duration, audio time, characters, requests, processing mode, or another provider-specific unit. Retries and unusable output can matter as much as the headline rate.
Fine-tuning and self-hosted inference introduce a different set of costs: training runs, evaluation, retraining, GPU capacity, utilization, idle time, storage, setup, and ongoing inference.
Choose the tool that matches the workload or cost question you are trying to understand. Each page explains the assumptions and limits that matter for that calculation.
Estimate OpenAI API costs using requests, input tokens, cached input tokens, output tokens, and model pricing.
Estimate Anthropic Claude API costs using input tokens, prompt caching, output tokens, Batch API, and monthly usage.
Estimate Google Gemini API costs using input tokens, cached input, output tokens, Batch API, prompt size, and monthly usage.
Estimate DeepSeek V4 Flash and V4 Pro API costs using cache-hit input, cache-miss input, output tokens, requests, and monthly usage.
Estimate xAI Grok API costs using requests, input tokens, cached input tokens, output tokens, and monthly usage.
Estimate Mistral API costs using input tokens, output tokens, Batch API, requests, and monthly usage.
Estimate Perplexity Sonar API costs using tokens, search context fees, Deep Research usage, requests, and monthly volume.
Estimate Cohere Command API costs using input tokens, output tokens, requests, and monthly usage.
Estimate AI API costs using input tokens, cached input tokens, output tokens, requests, and your own pricing.
Estimate AI image generation costs using price per image, monthly requests, retries, and other fixed costs.
Estimate the full cost of an AI voice agent using current rates you enter for speech-to-text, LLM, text-to-speech, telephony, platform, recording, setup, and fixed costs.
Compare OpenAI, Deepgram, AssemblyAI, Google Cloud, and custom speech-to-text costs using monthly audio hours and processing mode.
Compare current Google Veo 3.1 and Runway API video generation costs using clip duration, usable output, repeated attempts, budget, and custom pricing.
Estimate retrieval-augmented generation costs across chunking, embeddings, vector storage, reads, writes, reranking, LLM usage, refreshes, and setup.
Compare current OpenAI, Google Gemini, Mistral, Voyage AI, and custom embedding costs across indexing, refreshes, queries, and first-year usage.
Compare Voyage AI and Pinecone reranking costs, estimate downstream LLM token savings, and calculate the net monthly impact.
Estimate prompt caching savings across OpenAI, Claude, Google Gemini, and custom pricing using reusable tokens, cache hit rate, writes, storage, and monthly requests.
Estimate standard versus batch AI processing costs across OpenAI, Claude, Gemini, Mistral, and custom pricing, including repeat processing, setup, and break-even savings.
Estimate multi-step AI agent costs across planner and worker models, context growth, retries, paid tools, memory, human review, infrastructure, and product margin.
Estimate dataset, training, evaluation, retraining, tuned-model inference, hosting, payback, break-even usage, and first-year fine-tuning costs.
Compare an all-premium AI workflow with a low-cost and premium model routing strategy, including pass rate, fallback, retries, gateway fees, setup, and break-even savings.
Estimate candidate-model inference, model-grader calls, repeated evaluation runs, selective human review, platform costs, setup, and first-year evaluation spend.
Estimate input and output safety checks, policy-model grading, regeneration, human review, platform fees, setup, break-even blocking, and total guarded AI cost.
Compare current OpenAI, Claude, Google Gemini, and custom web search grounding costs across search calls, model tokens, retrieved context, retries, and free allowances.
Estimate OCR, extraction, vision, LLM validation, retries, human review, setup, break-even volume, and first-year document automation costs.
Estimate planning, coding, repair, review, CI, human approval, setup, cost per successful task, manual savings, payback, and break-even volume.
Estimate AI-handled support conversations, ticket deflection, model and retrieval spend, escalations, QA review, setup, savings, payback, and break-even automation share.
Estimate translation-model tokens, terminology lookup, QA, retries, human post-editing, setup, savings, payback, and break-even multilingual content volume.
Estimate generation, validation, deduplication, human review, rejected candidates, setup, accepted-record cost, savings, payback, and break-even dataset volume.
Compare full conversation history with summarized context using token growth, cached prefixes, summary overhead, context limits, overflow turns, monthly cost, and savings.
Estimate transcription, diarization, summaries, action items, storage, integrations, human review, setup, savings, payback, and break-even meeting volume.
Estimate GPUs per replica, throughput-based GPU hours, batching, utilization, idle capacity, self-hosted cost, managed API comparison, payback, and break-even volume.
A lower published rate does not automatically mean a lower monthly cost. Compare providers or models using the same request volume, token mix, media volume, retry assumptions, and other workload inputs.
Caching, batch processing, free allowances, repeated attempts, search calls, human review, and infrastructure can materially change the result. If one estimate includes those costs and another does not, the totals are not directly comparable.
Cost is also only one part of an AI decision. Quality, latency, context limits, reliability, privacy requirements, provider limits, and operational effort can matter just as much as the calculated amount.
A Beeija result is a planning estimate, not a provider invoice. Provider pricing, regions, service tiers, discounts, free allowances, taxes, custom agreements, and actual usage can change the final amount.
When provider rates are built into a tool, the goal is to make the pricing source, checked date, assumptions, and editable inputs clear enough that you can understand what is driving the result.
AI workloads often depend on compute, storage, databases, networking, or serverless infrastructure. Use the cloud category when those costs need to be planned separately.
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