If you are building with AI APIs, understanding token-based pricing is not optional -- it is the single biggest factor that determines whether your project is economically viable at scale. This guide covers everything from what tokens actually are to advanced strategies for cutting your AI costs by 50-80%.

What Are Tokens?

A token is a chunk of text that a language model processes as a single unit. Tokens are not words -- they are subword pieces created by a model's tokenizer. In English, one token is roughly 3-4 characters or about 0.75 words. The sentence "Hello, how are you today?" is 7 tokens. A 1,000-word blog post is approximately 1,300-1,500 tokens.

Different models use different tokenizers, which means the same text may produce slightly different token counts across providers. OpenAI uses the tiktoken library (cl100k_base encoding for GPT-4o), while Anthropic uses its own tokenizer. The differences are small -- typically within 5-10% -- but they matter at scale.

Key rule of thumb: 1 million tokens is roughly 750,000 words, or about 1,500 pages of text.

Input vs Output Pricing

Every major API charges differently for input tokens (what you send to the model) and output tokens (what the model generates). Output tokens are always more expensive -- typically 2-5x the input price -- because generating text requires more computation per token than reading it.

Here is the current pricing landscape as of early 2026 (per million tokens):

Frontier Models - GPT-4o: $2.50 input / $10.00 output - Claude 3.5 Sonnet: $3.00 input / $15.00 output - Gemini 2.0 Pro: $1.25 input / $10.00 output - Gemini 2.0 Flash: $0.10 input / $0.40 output

Mid-Tier Models - GPT-4o mini: $0.15 input / $0.60 output - Claude 3.5 Haiku: $0.80 input / $4.00 output - Gemini 2.0 Flash Lite: $0.075 input / $0.30 output

Budget and Open Source (via API providers) - Llama 3.3 70B (via Together): $0.88 input / $0.88 output - DeepSeek-V3 (via DeepSeek API): $0.27 input / $1.10 output - Qwen 2.5 72B (via Together): $0.90 input / $0.90 output - Mistral Large (via Mistral): $2.00 input / $6.00 output

Check our full pricing comparison page for live, up-to-date numbers across all models.

Understanding Your Cost Drivers

Your total API cost is: (input_tokens x input_price) + (output_tokens x output_price). But understanding which factor dominates your bill requires knowing your application's token ratio.

Chat applications Chat applications are typically output-heavy. A user sends a short message (50-200 input tokens including system prompt) and the model generates a longer response (200-800 output tokens). Your output costs will dominate. Optimizing output length through prompt engineering or using shorter system prompts has the biggest impact.

Document analysis RAG (Retrieval Augmented Generation) and document analysis workloads are input-heavy. You might send 10,000+ input tokens (the document plus context) and receive only 200-500 output tokens (the summary or answer). Here, input cost dominates, and choosing a model with cheaper input pricing matters more.

Code generation Code generation sits in between. Input prompts can be long (code context, file contents, instructions) and output can be substantial (generated code, explanations). Both input and output pricing matter.

The Context Window Tax

Context windows are a hidden cost driver. When you include conversation history, RAG context, or long system prompts, every token in the context window is billed as input for every single API call. A 4,000-token system prompt that runs on every request means 4,000 input tokens billed per call before the user even types anything.

For a customer support bot handling 10,000 conversations per day with a 4,000-token system prompt: - System prompt cost alone: 10,000 x 4,000 = 40M input tokens/day - At GPT-4o pricing: 40M x $2.50/1M = $100/day just for system prompts - At Claude Haiku pricing: 40M x $0.80/1M = $32/day

This is why choosing the right model tier for each task matters enormously. Not every request needs a frontier model.

Seven Strategies for Reducing AI Costs

1. Use Model Routing

The most effective cost reduction strategy is using different models for different tasks. Route simple classification tasks to GPT-4o mini ($0.15/M input) and reserve GPT-4o ($2.50/M input) for complex reasoning. A well-designed router can cut costs by 60-80% with minimal quality loss.

2. Leverage Batch APIs

Both OpenAI and Anthropic offer batch processing at 50% discounts. If your workload is not latency-sensitive (content moderation, data processing, scheduled analysis), batch APIs are free savings. OpenAI's batch API returns results within 24 hours; Anthropic's within a similar window.

3. Implement Prompt Caching

Anthropic offers prompt caching for Claude, where repeated prefixes in your prompts are cached and billed at a reduced rate (roughly 90% cheaper for cached tokens). If your system prompt or common context is the same across many requests, this can dramatically reduce input costs. OpenAI offers a similar feature with automatic prefix caching on GPT-4o.

4. Optimize Prompt Length

Every unnecessary word in your system prompt costs money at scale. Audit your prompts ruthlessly: - Remove redundant instructions - Use concise formatting directives - Test shorter prompts against longer ones -- you may find that a 500-token prompt performs as well as a 2,000-token one

5. Cache Responses

If your application generates the same or similar responses for common queries, implement application-level caching. A simple key-value cache for frequent questions can eliminate repeated API calls entirely.

6. Control Output Length

Use the max_tokens parameter to cap output length. If your task only needs a one-sentence answer, setting max_tokens to 100 prevents the model from generating a five-paragraph essay you pay for but discard. Prompt engineering that instructs the model to be concise is also effective.

7. Consider Open Source

For high-volume, latency-tolerant workloads, self-hosting an open source model like Llama 3 70B or Qwen 2.5 72B can be dramatically cheaper. The upfront infrastructure cost (GPU instances) is fixed, so your per-token cost decreases as volume increases. At roughly 50,000+ requests per day, self-hosting typically becomes cheaper than API pricing. Our open source vs closed source analysis covers this tradeoff in detail.

Pricing Trends

AI model pricing has dropped precipitously since 2023. GPT-4 launched at $30/$60 per million tokens; GPT-4o now costs $2.50/$10.00 -- roughly a 12x reduction in under two years. Claude pricing has followed a similar curve. This trend is expected to continue as inference efficiency improves and competition intensifies.

For budgeting purposes, assume that today's frontier pricing will be tomorrow's mid-tier pricing within 12-18 months. Build your cost models with this deflationary trend in mind.

Calculating Your Monthly Bill

Here is a formula to estimate your monthly AI API costs:

Monthly Cost = (avg_input_tokens_per_request x requests_per_day x 30 x input_price_per_token) + (avg_output_tokens_per_request x requests_per_day x 30 x output_price_per_token)

Example: A chatbot averaging 2,000 input tokens and 500 output tokens per request, handling 5,000 requests/day on GPT-4o: - Input: 2,000 x 5,000 x 30 x $2.50/1,000,000 = $750/month - Output: 500 x 5,000 x 30 x $10.00/1,000,000 = $750/month - Total: $1,500/month

The same workload on GPT-4o mini: - Input: 2,000 x 5,000 x 30 x $0.15/1,000,000 = $45/month - Output: 500 x 5,000 x 30 x $0.60/1,000,000 = $45/month - Total: $90/month

That is a 94% cost reduction. If GPT-4o mini handles your use case adequately, the savings are enormous.

Use our pricing comparison tool to model different scenarios and find the optimal model for your budget.

Key Takeaways

  • •Output tokens cost 2-5x more than input tokens -- optimize output length first
  • •Model routing (using cheaper models for simple tasks) is the single biggest cost lever
  • •Batch APIs and prompt caching offer 50-90% savings for eligible workloads
  • •Self-hosting open source models becomes cost-effective at roughly 50K+ daily requests
  • •AI pricing is dropping 5-10x every 18 months -- build cost projections accordingly

Visit our pricing page for live pricing across every model in our directory, or use the comparison tool to find the best model for your budget.