AI API Pricing Calculator

Artificial Intelligence APIs have revolutionized how developers integrate machine learning capabilities into applications. However, understanding the cost structure across different providers can be challenging. This comprehensive calculator helps you compare AI API pricing from major providers including OpenAI, Anthropic, Google, DeepSeek, Meta, Amazon, Microsoft, and many others across chat models, embeddings, speech synthesis, transcription, and image generation.

What Are AI API Costs?

AI API costs vary by model type. Chat and language models are typically priced per token—pieces of text the AI processes for both input (prompts) and output (responses). Most providers charge separately for input and output tokens, with output tokens costing more due to generation complexity. Speech models are priced per character (text-to-speech) or per minute (speech-to-text). Image generation models are priced per image created. Embedding models are priced per input token only, as they don't generate text output.

How Token Pricing Works

Providers price language models per million tokens (1M tokens). For example, if a model costs $0.50 per 1M input tokens and you send 10,000 tokens, your cost would be $0.005. The calculator above lets you toggle between per-million and per-thousand pricing for easier comparison. Understanding this calculation is crucial for budgeting your AI integration projects effectively.

Comparing Major AI Providers

OpenAI

OpenAI offers a comprehensive range of models from the cost-effective GPT-4o-mini ($0.15/$0.60 per 1M tokens) to the powerful GPT-4o ($2.50/$10.00) and GPT-4 Turbo ($10.00/$30.00). The o1 series provides advanced reasoning capabilities with o1-mini ($3.00/$12.00) for faster tasks and o1 ($15.00/$60.00) for complex reasoning. For premium reasoning, o1-pro ($150.00/$600.00) offers the highest capability. OpenAI also provides DALL-E for image generation ($0.016-$0.120 per image), Whisper for transcription ($0.006 per minute), and text-to-speech ($15.00-$30.00 per 1M characters).

Anthropic Claude

Anthropic's Claude models are known for strong reasoning and analysis. The Claude 3 family includes Haiku ($0.25/$1.25 per 1M tokens—fastest and most affordable), Sonnet ($3.00/$15.00—balanced performance), and Opus ($15.00/$75.00—most capable). The Claude 3.5 series offers enhanced capabilities with Haiku ($0.80/$4.00) and Sonnet ($3.00/$15.00) providing excellent value for complex tasks with 200K context windows.

Google Gemini

Google's Gemini models provide excellent multimodal capabilities. Gemini 1.5 Flash ($0.075/$0.30 per 1M tokens) offers exceptional speed and cost efficiency with a 1M token context window, making it ideal for high-volume applications. Gemini 1.5 Pro ($1.25/$5.00) delivers enterprise-grade performance with an impressive 2M token context window. Gemini 1.0 Pro ($0.50/$1.50) provides a balanced option for standard tasks. All Gemini 1.5 models support text, vision, and audio inputs.

DeepSeek

DeepSeek offers highly competitive pricing with exceptional value. DeepSeek-V3 ($0.27/$1.10 per 1M tokens) provides strong general-purpose performance at a fraction of competitors' costs. DeepSeek-R1 ($0.55/$2.19) offers advanced reasoning capabilities at prices significantly lower than OpenAI's o1 series. DeepSeek-Coder ($0.27/$1.10) is optimized for programming tasks. All models feature 64K context windows and are particularly well-suited for coding, technical tasks, and cost-sensitive applications.

Meta Llama

Meta's Llama models are open-source and available through various API providers like Together AI and Fireworks AI. The Llama 3.1 series offers strong performance with 8B ($0.20/$0.20), 70B ($0.88-$0.90), and 405B ($3.00-$3.50) parameter models. Llama 3.2 includes smaller models like 1B and 3B ($0.10/$0.10) for cost-effective tasks, plus vision-capable models like 11B Vision ($0.35/$0.35) and 90B Vision ($1.20/$1.20). Llama 3.3 70B ($0.88/$0.88) offers the latest improvements. All models feature 128K context windows and provide excellent value for open-source flexibility.

Amazon Bedrock

Amazon's Nova series provides cloud-native AI models with competitive pricing. Nova Micro ($0.035/$0.14 per 1M tokens) offers the most affordable option for simple tasks. Nova Lite ($0.06/$0.24) adds vision capabilities with a 300K context window. Nova Pro ($0.80/$3.20) delivers advanced multimodal understanding for complex applications. AWS Bedrock also hosts third-party models including Claude 3.5 Sonnet, Claude 3 Haiku, and Mistral models.

Microsoft Azure

Microsoft offers Phi models optimized for efficiency. Phi-3 Mini 128K ($0.10/$0.10 per 1M tokens) and Phi-3.5 Mini ($0.10/$0.10) provide cost-effective performance for standard tasks. Phi-3 Medium 128K ($1.00/$1.00) offers enhanced capabilities. Azure OpenAI Service also provides access to GPT-4o, GPT-4o-mini, GPT-4 Turbo, and o1 models with enterprise features and regional deployment options.

Cohere

Cohere specializes in enterprise language AI. Command R ($0.15/$0.60 per 1M tokens) offers cost-effective performance with 128K context. Command R+ ($3.00/$15.00) provides enhanced capabilities for complex tasks. Command Light ($0.30/$0.60) and Command ($1.00/$2.00) serve different use cases. Cohere also offers embedding models at $0.10 per 1M tokens for both English and multilingual applications.

Mistral AI

Mistral provides European-based AI models. Mistral Large 2 ($2.00/$6.00 per 1M tokens) offers strong performance with 128K context. Mistral Small ($0.20/$0.60) provides an affordable option for simpler tasks with 32K context. Mistral models are available through multiple cloud providers including AWS Bedrock and Azure.

Speech and Audio Models

Text-to-Speech: OpenAI offers TTS ($15.00 per 1M characters) and TTS HD ($30.00 per 1M characters). ElevenLabs provides Multilingual v2 ($0.30 per 1K characters) and Turbo v2.5 ($0.20 per 1K characters) with high-quality voice synthesis. Azure Neural TTS costs $16.00 per 1M characters. Speech-to-Text: OpenAI's Whisper costs $0.006 per minute. Azure Speech-to-Text costs $1.00 per hour. These models are priced by character count or duration, not tokens.

Image Generation Models

OpenAI DALL-E: DALL-E 3 ranges from $0.040 (1024×1024) to $0.120 (HD 1024×1792) per image. DALL-E 2 offers more affordable options from $0.016 to $0.020 per image. Stability AI: Stable Diffusion models range from $0.010 (SD 1.6) to $0.080 (Stable Image Ultra) per image. SD 3.5 Large ($0.065) and SD 3.5 Large Turbo ($0.040) offer the latest generation quality. All image models are priced per image generated, not per token.

Embedding Models

Embeddings convert text into numerical vectors for semantic search and similarity. OpenAI offers text-embedding-3-small ($0.02 per 1M tokens, 1536 dimensions), text-embedding-3-large ($0.13, 3072 dimensions), and ada-002 ($0.10, 1536 dimensions). Cohere provides embed-english-v3.0 and embed-multilingual-v3.0 ($0.10, 1024 dimensions). Mistral-embed costs $0.10 per 1M tokens. Voyage AI and Jina AI offer specialized embeddings from $0.02 to $0.12 per 1M tokens. Embeddings have no output cost since they don't generate text.

Cost Optimization Strategies

1. Choose the Right Model for Each Task

Not every task requires the most powerful model. Simple classification, summarization, or basic text generation can often be handled by smaller, more affordable models like GPT-4o-mini, Gemini 1.5 Flash, or Llama 3.2 3B. Reserve premium models like GPT-4o, Claude 3.5 Sonnet, or o1 for complex reasoning, analysis, or creative tasks. Use the calculator above to compare costs across different models for your specific usage patterns.

2. Optimize Prompt Length and Structure

Since you pay for both input and output tokens, crafting concise, effective prompts can significantly reduce costs. Remove unnecessary context, avoid repetition, and use clear, direct instructions to minimize token usage while maintaining quality. Consider using system messages to set context once rather than repeating instructions in every prompt. For embeddings, preprocess text to remove unnecessary formatting.

3. Implement Caching and Deduplication

For repeated queries or similar requests, implement caching mechanisms to avoid redundant API calls. This can dramatically reduce costs for applications with predictable patterns, frequently asked questions, or repeated content. Store embeddings for documents you process multiple times. Cache common responses and use semantic similarity to match new queries to cached results.

4. Use Batch Processing and Async Operations

When possible, batch multiple requests together or use asynchronous processing. This reduces overhead and can be more cost-effective than processing items individually, especially for large-scale operations. Some providers offer batch API endpoints with reduced pricing. For embeddings, process documents in batches rather than one at a time.

5. Set Token Limits and Implement Fallbacks

Set maximum token limits for responses to prevent unexpectedly long (and expensive) outputs. Implement fallback strategies where you try a cheaper model first and only escalate to more expensive models if needed. Monitor response quality and adjust your model selection strategy based on actual performance requirements.

6. Monitor and Analyze Usage Patterns

Regularly review your API usage patterns using provider dashboards. Identify high-cost operations and optimize them. Track which models and prompts provide the best value for your specific use cases. Set up alerts for unusual spending patterns. Use the calculator above to model different scenarios and find the optimal balance between cost and capability.

7. Consider Multi-Provider Strategies

Don't lock yourself into a single provider. Different providers excel at different tasks and offer different pricing structures. Use DeepSeek for cost-sensitive applications, Gemini Flash for high-volume tasks, Claude for complex reasoning, and specialized models for specific needs like embeddings or image generation. The calculator helps you compare costs across all providers.

Understanding Token Conversion

Tokens don't directly correspond to words or characters. As a general rule:

• 1 token ≈ 0.75 words (or 1 word ≈ 1.3 tokens)
• 1 token ≈ 4 characters for English text
• Complex words, technical terms, and non-English text may use more tokens
• Code and structured data (JSON, XML) typically use more tokens per character
• Use the calculator's word and character modes to estimate token usage for your content

Understanding Different Pricing Models

Token-based pricing (chat, embeddings): You pay per million tokens processed. Input and output are usually priced separately. Character-based pricing (text-to-speech): You pay per thousand or million characters of text converted to speech. Duration-based pricing (speech-to-text): You pay per minute or hour of audio transcribed. Per-image pricing (image generation): You pay a fixed amount per image generated, sometimes varying by resolution. Per-call fees: Some models (like Perplexity Sonar) charge an additional fee per API call on top of token costs.

Enterprise Considerations

For enterprise deployments, consider these additional factors:

• Volume Discounts: Many providers offer reduced rates for high-volume usage or enterprise agreements
• SLA Guarantees: Premium tiers often include uptime guarantees, priority support, and dedicated resources
• Data Privacy: Cloud providers (Azure, AWS, Google) offer private deployment options with data residency guarantees
• Custom Models: Fine-tuned models may have different pricing than base models, plus training costs
• Regional Pricing: Costs may vary by geographic region, especially for cloud-hosted models
• Batch API Discounts: Some providers offer 50% discounts for batch processing with longer turnaround times

Free Tiers and Development Options

Most major providers offer free trial credits for new users to help evaluate their models during development and testing. OpenAI typically provides $5-$18 in free credits. Google offers free tier access to Gemini models with rate limits. Anthropic provides trial credits for Claude. These trials are perfect for prototyping and optimization. For production use, expect to transition to paid plans based on your usage volume and requirements. Some open-source models can be self-hosted for free, though this requires infrastructure investment.

Currency and Regional Considerations

While most providers price in USD, some regional providers use local currencies. Chinese providers like Alibaba Cloud (Qwen models) and Zhipu AI (GLM models) price in CNY (Chinese Yuan). The calculator automatically converts these to USD for comparison (using approximate rates). Regional pricing may also vary for cloud providers—Azure, AWS, and Google Cloud may have different rates in different regions. Always verify current pricing in your region and currency before making final decisions.

Future Pricing Trends

AI API pricing continues to evolve rapidly. We're seeing trends toward:

• More competitive pricing as the market matures and new providers enter
• Specialized models optimized for specific tasks (coding, reasoning, vision) at lower costs
• Increased availability of free and open-source alternatives with commercial hosting options
• More transparent and predictable pricing structures with better cost estimation tools
• Tiered pricing based on speed, quality, and capability rather than one-size-fits-all
• Greater price competition in the reasoning model space following DeepSeek's aggressive pricing