AI API Cost Calculator & Comparator

• AI inference costs vary 50–300× between models — choosing the wrong one can mean $500 vs $50,000 per month
• Output tokens are typically 4–10× more expensive than input — your output:input ratio drives total cost more than you'd guess
• Cached-input pricing (offered by OpenAI, Anthropic, Google) is a ~90% discount and is often overlooked in cost estimates
• Cheap doesn't mean bad: DeepSeek V3, Gemini Flash, and Haiku 4.5 can match GPT-4o quality for 5–20% of the cost on many tasks
• Reasoning models (o3, DeepSeek R1) bill thinking tokens as output — real cost can be 2–10× higher than naïve estimates
• 1M-context windows (Gemini 2.5 Pro, Claude Opus 4.7, Llama 4 Scout 10M) are great for RAG but punish per-call costs
• Llama 4 / Mistral via Together / Groq are typically the cheapest frontier-quality option for open-source workloads

Use Cases

Frequently Asked Questions

How accurate are the AI API cost estimates?

Estimates are accurate to within ~5% of your actual provider invoice when your inputs are correct, assuming standard (non-reasoning) models and no enterprise discounts. The calculator uses each provider's published per-million-token rates and models cached-input discounts. For reasoning models (o3, DeepSeek R1, extended thinking on Claude), output token counts in real usage are typically 2–10× higher than your visible response length because thinking tokens bill as output — add a safety margin. Provider prices also change frequently; we update this tool when major providers publish changes, but always verify with the provider's official pricing page before signing contracts.

Which AI API is cheapest for a production chatbot?

For most production chatbots in 2026: Claude Haiku 4.5 ($1 input / $5 output per million), Gemini 2.5 Flash ($0.30 / $2.50), or GPT-5 nano ($0.05 / $0.40) deliver strong quality at low cost. For absolute cheapest hosted frontier-quality, look at DeepSeek V3 ($0.27 / $1.10) or Llama 4 Maverick via Together AI ($0.27 / $0.85). Run a realistic eval against your actual chat traffic before committing — quality varies significantly by task. Don't forget cached-input pricing: for a chatbot with a fixed system prompt, enabling caching often saves more money than switching models.

Why is Claude Opus 4.7 so much more expensive than GPT-5?

Anthropic has historically positioned Opus as their premium flagship at premium pricing — $15/$75 per million input/output vs OpenAI's GPT-5 at $1.25/$10. The pricing reflects Anthropic's bet that for the hardest reasoning and coding tasks, customers will pay 10× the price for incrementally better quality. For most tasks (chatbots, summarization, RAG, simple agents), Claude Sonnet 4.6 at $3/$15 delivers nearly identical quality to Opus at 1/5 the price. Reserve Opus for tasks where you can measurably justify the cost.

What is cached-input pricing and should I use it?

Cached-input pricing lets you pay ~90% less for input tokens that repeat across many requests — typically system prompts, RAG context, or few-shot examples. OpenAI applies it automatically when prompts share a prefix of ≥1024 tokens. Anthropic requires explicit cache_control blocks but offers the deepest discounts. Google's context caching API charges $0.0625/M for cached Gemini 2.5 Pro input vs $1.25/M uncached. Yes, you should use it — for any production AI feature with a stable system prompt, prompt caching can cut total cost by 50–80% with zero quality impact. The calculator's cacheable-share slider models this directly.

How do I count tokens for cost estimation?

Rule of thumb: 1 token ≈ 0.75 English words ≈ 4 characters. So a 1,000-word document is roughly 1,333 tokens. For exact counts, use the official tokenizer for your model — tiktoken for OpenAI, the Anthropic tokenizer API for Claude, Vertex AI's count_tokens for Gemini. Our Token to Word Converter (linked in related tools) provides quick estimates. For cost estimation, exact counts matter less than rough budgeting — being off by ±15% on token count is fine for decision-making; if you're off by 50% you're using the wrong formula.

Should I use OpenAI, Anthropic, or open-source models?

Depends on your workload. Closed models (OpenAI, Anthropic, Google) give you predictable APIs, strong safety filters, and dedicated support — but pay premium pricing and your data may flow through their training pipelines (read the terms). Open weights via hosted APIs (Llama 4, DeepSeek, Mistral via Together / Groq / Fireworks) are 5–20× cheaper and you can self-host or switch providers easily — but quality is task-dependent and you handle more operational complexity. Many serious production teams now run hybrid: closed models for hardest tasks, cheap hosted open weights for high-volume routine work. The cost calculator lets you model both side by side.

What's the difference between GPT-5 and GPT-5 mini for cost?

GPT-5 is OpenAI's flagship at $1.25/$10 per million input/output. GPT-5 mini is the cost-optimized variant at $0.25/$2 — 5× cheaper. GPT-5 nano sits even lower at $0.05/$0.40 — perfect for high-volume classification or simple tasks. The mini and nano variants typically retain 80–95% of GPT-5's quality on common tasks at 5–25× the cost reduction. For most production features, start with mini and only upgrade to full GPT-5 if your evals show meaningful quality wins.

Are reasoning models like o3 and DeepSeek R1 worth the cost?

Yes for hard reasoning tasks (multi-step math, code with subtle logic, complex agent planning), no for routine chat or summarization. The catch: reasoning models bill thinking tokens as output, which means a 200-token visible response can actually consume 1,500–5,000 output tokens. Your real per-request cost is often 3–10× higher than a naïve estimate. Budget accordingly. For the cheapest reasoning, DeepSeek R1 at $0.55/$2.19 dramatically undercuts OpenAI's o3 at $2/$8 — quality on coding tasks is competitive.

How do I share my cost comparison with my team?

Click 'Copy share link' to get a URL with your exact selected models, usage numbers, and cacheable percentage encoded in the query string. Anyone who opens the link sees the same comparison. For docs, decks, or Slack messages, click 'Copy comparison summary' to grab a plain-text version with monthly costs sorted cheapest-first plus a savings callout. Both options are 100% client-side — no account, no signup, no link shortener creating an external dependency.

Is my usage data private?

Yes. The calculator runs entirely in your browser using JavaScript. Your usage figures, selected models, and projected scale are never sent to any server, never logged, and never stored. Verify it yourself: open DevTools → Network tab and confirm zero outgoing requests when you change inputs. Safe for confidential business projections, fundraising decks, or any sensitive cost modeling.

How often is the pricing data updated?

Pricing is sourced from each provider's public pricing pages and refreshed whenever a major provider announces changes. The 'Last updated' date is shown above the model picker. AI pricing changes frequently — sometimes monthly — so always verify with the provider's official documentation before relying on these numbers for production budgeting or contract negotiations. If you spot an outdated price, the data lives in a single file (lib/ai-pricing-data.ts) and is easy to update.

Can I compare self-hosted vs hosted model costs?

Not directly. This calculator focuses on hosted commercial APIs where pricing is per-token. Self-hosting open-weight models has different economics: GPU hourly cost + utilization rate, not per-token. As a rough comparison, hosting Llama 4 70B on an 8x H100 cluster ($30-40/hour cloud) at 50% utilization breaks even with Together AI's $0.27/M input pricing at roughly 10–20M tokens/hour throughput. For high-volume continuous workloads self-hosting wins; for bursty or low-volume API calls, hosted is dramatically cheaper. Our LLM Training Cost Calculator covers self-hosted GPU economics in detail.