Understanding LLM API Pricing: Tokens, Completion Costs, and Extended Context Windows
In this article, we explore how pricing is structured—what tokens and completion prices are—as well as why extended context windows matter. We also provide a normalized pricing comparison across major providers like OpenAI, Anthropic, Google, and DeepSeek.
What Are Tokens?
At the core of LLM API pricing is the concept of a token. Tokens are the smallest individual units of text that these models process. A token might be a word, a number, a short fragment of a word, or even punctuation. For example, the sentence “Hello, world!” might be split into several tokens. Pricing for LLM APIs is typically charged on a per-1 million-token basis—both for the text you send into the model (prompt/input) and for the text it generates as a response (completion/output).
Defining Completion Price
When using an LLM API, you will notice that costs are generally divided into two parts:
- Prompt (Input) Tokens: These are the tokens that you send as your question or command.
- Completion (Output) Tokens: These are the tokens the model generates in response.
The completion price is the cost associated with the tokens that the model creates. In many cases, output tokens are more expensive than input tokens because generating text is computationally intensive. For instance, Anthropic’s Claude 3.7 Sonnet charges 15 per 1 million output tokens, reflecting its advanced processing capabilities.
The Importance of Extended Context Windows
A critical factor in LLM performance is the context window—the maximum number of tokens the model can consider at one time.
- Shorter Context Windows (e.g., 8K tokens): Ideal for standard tasks such as casual conversation or summarizing short documents.
- Extended Context Windows (e.g., 32K tokens or even up to 200K tokens): These are essential when dealing with long documents, detailed legal contracts, technical reports, or extensive conversation histories. Extended context windows enable the model to maintain coherence over long passages, improving the quality of outputs. However, they usually come at a higher price due to increased computational requirements.
Normalized Pricing Comparison Table
Below is a comparative table that details the standard (non-discounted) per-1 million-token pricing for major LLM API providers. This table includes key model variants across different providers, sorted by provider name.
| Provider | Model | Input Price (USD/1M tokens) | Output Price (USD/1M tokens) | Context Window |
|---|---|---|---|---|
| Anthropic | Claude 3.5 Haiku | $0.80 | $4.00 | 200K tokens |
| Anthropic | Claude 3.7 Sonnet | $3.00 | $15.00 | 200K tokens |
| Anthropic | Claude 3 Opus | $15.00 | $75.00 | 200K tokens |
| DeepSeek | deepseek-chat (V3) | $1.00 (standard rate) | $2.00 (standard rate) | 64K tokens |
| DeepSeek | deepseek-reasoner (R1) | $4.00 (standard rate) | $12.00 (standard rate) | 64K tokens (plus up to 32K CoT tokens) |
| Gemini 1.5 Flash | $1.50 (estimate) | $3.00 (estimate) | (Varies; advanced models often support ~1M tokens) | |
| Gemini 1.5 Pro | $25.00 (estimate) | $50.00 (estimate) | (Varies; typically very high capacity) | |
| OpenAI | GPT-3.5 Turbo | $1.50 | $2.00 | ~4K–8K tokens |
| OpenAI | GPT-4 8K context | $30.00 | $60.00 | 8K tokens |
| OpenAI | GPT-4 32K context | $60.00 | $120.00 | 32K tokens |
Notes:
- Pricing Structure: The table shows the base (standard) pricing with no discounts applied. For example, DeepSeek’s rates listed are for peak hours (UTC 00:30–16:30); off-peak rates are discounted but not included here.
- Context Windows Explained: OpenAI offers GPT-4 in both 8K and 32K modes, while Anthropic’s models boast up to 200K tokens. DeepSeek models support a 64K token window with additional capacity for chain-of-thought (CoT) reasoning in the deepseek-reasoner.
- Advanced Features: Some models such as Claude 3.7 Sonnet support hybrid reasoning (balancing quick responses with deep, step-by-step thought), but the billing remains per token processed regardless of extra computations.
- Estimates: Google’s pricing for Gemini models is estimated based on recent announcements and market comparisons.
Final Thoughts
Understanding how LLM API pricing works enables developers and businesses to estimate costs more accurately and choose the model that best suits their needs. Key takeaways include:
- Tokens are the basic billing units, with different costs for input (prompt) and output (completion).
- Completion Price is the fee for every token the model generates, often higher due to extra computational load.
- Extended Context Windows allow models to process longer inputs or maintain coherence over extended interactions, which is invaluable for complex tasks—even though they may drive up costs.
As you compare models, consider how these features align with your application’s requirements. Lower input and output prices can cut costs dramatically, but more advanced models with larger context windows may provide the deeper understanding and continuity needed for sophisticated tasks.
By keeping these factors in mind and using normalized pricing data like the table above, you can make more informed decisions for your next AI-powered project.