Optimize Your AI Spending
Compare all models side by side to find the best cost-performance ratio for your use case.
Image APIs are priced per image, not per token. Enter your monthly volume to estimate costs across providers.
Set your typical usage and we'll rank every major model by monthly cost.
What's a Healthy AI Cost Ratio?
Under 10% — Excellent. Strong unit economics with room to scale or improve the product.
10–20% — Acceptable. Most sustainable AI SaaS products operate here. Watch for cost creep as usage grows.
Over 20% — Risky. Consider switching to a cheaper model for routine tasks, adding per-user usage limits, or repricing. Going viral at this ratio will hurt.
Find a Cheaper Model
See how much you'd save switching models — without changing anything else.
What Is an API Token?
A token is the fundamental unit that AI language models use to process and generate text. One token is roughly 4 characters, or about three-quarters of a word in English. A typical sentence contains 15–20 tokens, a paragraph around 100–150 tokens, and a full page of text roughly 750 tokens.
AI APIs charge separately for input tokens — everything you send to the model including your prompt, system instructions, and conversation history — and output tokens, which are the words the model generates in response. Output tokens are typically priced 3–5x higher than input tokens because generation is more computationally expensive than reading.
Understanding the input/output split matters for cost optimization. A chatbot application with long conversation histories accumulates input tokens fast. A summarization tool generates few output tokens relative to what it reads. Knowing your ratio helps you pick the right model and estimate costs accurately.
How AI API Pricing Works
All major AI APIs use a pay-per-token model where you're charged per million tokens processed. The formula is straightforward:
Monthly Cost = (Input tokens × Input rate) + (Output tokens × Output rate) ÷ 1,000,000
For example: 10,000 API calls per month, 500 input tokens and 200 output tokens per call on GPT-4o ($2.50/$10.00 per 1M) = (5M × $2.50) + (2M × $10.00) ÷ 1M = $12.50 + $20.00 = $32.50/month.
| Provider | Model | Input / 1M | Output / 1M | Tier |
|---|---|---|---|---|
| OpenAI | GPT-4o | $2.50 | $10.00 | Premium |
| OpenAI | GPT-4o mini | $0.15 | $0.60 | Budget |
| Anthropic | Claude Sonnet 4.6 | $3.00 | $15.00 | Premium |
| Anthropic | Claude Haiku 3 | $0.25 | $1.25 | Budget |
| Gemini 2.5 Flash | $0.30 | $2.50 | Budget | |
| Gemini 2.0 Flash | $0.10 | $0.40 | Cheapest | |
| Meta / Groq | Llama 3 70B | $0.59 | $0.79 | Budget |
| Mistral | Mistral Large | $3.00 | $9.00 | Premium |
Pricing last verified April 2026. Always confirm current rates on each provider's pricing page.
Image Generation API Pricing
Unlike text APIs, image generation APIs are priced per image rather than per token. Costs vary significantly by model, resolution, and quality setting — from under half a cent for open-source models to $0.12 for premium DALL-E 3 outputs.
| Provider | Model | Standard | HD / Large | Notes |
|---|---|---|---|---|
| OpenAI | DALL-E 3 | $0.040 | $0.080–$0.120 | Best prompt adherence |
| OpenAI | DALL-E 2 | $0.020 | $0.028 | Faster, lower quality |
| Replicate | Flux Schnell | $0.003 | $0.006 | Fastest, great for drafts |
| Replicate | Flux Dev | $0.025 | $0.050 | High quality open source |
| Replicate | SDXL | $0.009 | $0.018 | Widely supported |
| Ideogram | Ideogram v2 | $0.080 | $0.160 | Best for text in images |
For high-volume image generation pipelines, Flux Schnell via Replicate offers the lowest per-image cost. For consumer-facing applications requiring high quality and strong prompt adherence, DALL-E 3 remains the most reliable choice. Ideogram is the standout option when your images need to contain readable text.
Batch API Pricing — 50% Off for Async Workloads
Both OpenAI and Anthropic offer batch processing APIs that cut costs in half in exchange for asynchronous delivery — results are returned within 24 hours rather than in real time.
Batch APIs are ideal for any workload that doesn't require an immediate response: data processing pipelines, bulk content generation, overnight analysis jobs, large-scale classification tasks, and document summarization at scale. If your application can tolerate a delay, batch mode is one of the easiest cost reductions available.
- OpenAI Batch API: 50% off all GPT-4o and GPT-4o mini pricing. Files up to 100MB, results within 24 hours.
- Anthropic Batch API: 50% off all Claude models. Results within 24 hours, up to 10,000 requests per batch.
- Google: Does not currently offer a batch discount tier — standard pricing applies to all requests.
Use the batch toggle in the Cost Estimator tab to see how much you'd save by switching eligible workloads to batch processing.
Choosing the Right Model for Your Use Case
The biggest cost lever most developers haven't pulled is model selection. Premium models like GPT-4o and Claude Sonnet are 10–20x more expensive than their smaller counterparts — and for many tasks, the quality difference is negligible.
| Use Case | Recommended Model | Why |
|---|---|---|
| Simple Q&A, classification | GPT-4o mini or Gemini Flash | Cheaper, fast, sufficient accuracy |
| Long document analysis | Gemini 2.5 Flash | Large context window, low cost |
| Structured output / JSON | Claude Haiku 3 | Strong instruction following |
| Complex reasoning | GPT-4o or Claude Sonnet | Premium models justify cost |
| High volume / low latency | Gemini 2.0 Flash | Cheapest per token, fast |
| Code generation | GPT-4o or Claude Sonnet | Best code quality |
| Batch / async processing | GPT-4o mini (batch) | 50% off + sufficient quality |
The practical approach: build with a premium model first to establish baseline quality, then systematically test cheaper models on each task type. Most production applications end up using a mix — premium models for complex tasks, cheaper models for routine ones.
How to Reduce Your AI API Costs
Most teams overspend on AI APIs before they've optimized. Here are the highest-impact strategies, roughly in order of effort vs. return.
- Downgrade models for routine tasks. If 80% of your calls are simple classification or summarization, switching those to GPT-4o mini or Gemini Flash saves 10–20x with minimal quality loss. Reserve premium models for tasks that actually need them.
- Use batch APIs for non-real-time workloads. Any job that doesn't need an instant response — data pipelines, content generation, bulk analysis — qualifies for 50% off via OpenAI or Anthropic's batch APIs.
- Trim your system prompt. System prompts are sent with every request. A 500-token system prompt on 100,000 calls per month adds 50M tokens of unnecessary input cost. Audit and compress your prompts — most can be cut by 30–50% without losing behavior.
- Cache frequent responses. If users repeatedly ask similar questions, cache the model's response rather than calling the API again. Even simple hash-based caching on common queries can cut call volume significantly.
- Set output length limits. Many models over-generate by default. Setting a max_tokens limit appropriate to your use case prevents unnecessarily long outputs that inflate costs.
- Profile before optimizing. Add logging to track actual token counts per request. Many teams discover their estimates are off by 2–3x in either direction once they measure real usage.