An LLM pricing comparison lets developers, founders, and teams evaluate AI model costs before committing to an API provider. With frontier model prices ranging from $0.10 to $15+ per million tokens and context windows from 8K to 2M tokens, choosing the right model can mean a 10–100× difference in monthly API spend. Compare all major models below and estimate your real monthly cost.
11 models shown
| Model | Provider | Input $/M | Output $/M | Context | Speed |
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Prices are published API rates as of early 2026. Verify with each provider before budgeting. $/M = dollars per million tokens.
Monthly Cost Estimator
Enter your daily usage to see monthly API cost across all models
Total requests per day across all users
Prompt + context tokens per request
Response tokens generated per request
| Model | Monthly Cost | Per Call | Annual Cost |
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Costs are estimates based on published per-token pricing. Some providers offer volume discounts not reflected here.
How to Use the LLM Pricing Comparison Tool
Choosing the right LLM API for your project isn't just about capability — the pricing difference between models can be 50–100× for the same task. This LLM pricing comparison tool helps developers, product teams, and indie builders estimate real costs before committing to a provider, based on published 2026 API rates.
Step 1: Browse and Filter the Comparison Table
The main comparison table shows all 11 models with their input price, output price, context window size, and speed tier. Use the filter bar to narrow results by provider (OpenAI, Anthropic, Google, Meta, Mistral), context window requirement, price tier (budget/mid/premium), or speed. Click any column header to sort the table — click again to reverse the sort order.
Step 2: Enable "Highlight Cheapest" for Quick Scanning
Check the Highlight cheapest per category checkbox to visually mark the lowest-cost model for input tokens, output tokens, and best context value. This instantly shows which model wins on each dimension without manually scanning the table.
Step 3: Estimate Your Monthly Cost
Scroll to the Monthly Cost Estimator section and enter: daily API calls (how many requests your app makes per day), average input tokens per call (your prompt + any context you send), and average output tokens per call (the response length). The estimator calculates the monthly cost for every model in the table, sorted from cheapest to most expensive. This makes it easy to see the real-world cost difference between models for your specific workload.
Understanding Input vs. Output Token Pricing
Every LLM API charges separately for input tokens (your prompt) and output tokens (the model's response). Output tokens almost always cost 2–5× more per token than input tokens. For workloads where the model generates long responses — such as writing, summarization, or code generation — the output token cost dominates. For retrieval-augmented generation (RAG) with short answers, the input cost (long document context) may dominate. Understanding your input/output ratio is key to accurate cost modeling.
Context Window: Why It Matters for Cost
The context window determines how much text you can include in a single API call. Larger context windows let you include more conversation history, longer documents, or more few-shot examples — but every token in the context window is counted as input and billed accordingly. Models with 1M+ context windows (Gemini 1.5 Pro, Gemini 1.5 Flash) are powerful for long-document workloads but can be expensive if you always fill the context. Right-sizing your context is often the highest-impact cost optimization.
Speed Tiers Explained
Fast models (GPT-4o-mini, Claude 3 Haiku, Gemini 1.5 Flash, Llama via Groq) typically return first tokens in under 500ms and generate 100–200+ tokens per second — ideal for real-time user interfaces and high-throughput pipelines. Medium speed models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) offer frontier intelligence with acceptable latency for most production apps. Slow models (GPT-4, Claude Opus, Mistral Large at high load) are best suited for batch workloads where quality matters more than speed.
Budget vs. Premium Models: When to Choose Each
Budget models (GPT-4o-mini at $0.15/M input, Claude 3 Haiku at $0.25/M input) are ideal for classification, simple Q&A, extraction, and any task where the model doesn't need deep reasoning. Premium models (GPT-4o at $2.50/M, Claude Opus at $15/M) justify their cost for complex reasoning, code generation, nuanced writing, and tasks where errors are expensive. A common architecture is to route simple queries to a budget model and complex ones to a premium model, reducing overall cost by 70–90% while maintaining quality where it matters.
FAQ
Is this LLM pricing comparison tool free?
Yes, completely free with no signup or account required. All pricing data is embedded in the tool and all calculations run locally in your browser. No data is sent to any server.
Is my data private when using this tool?
Absolutely. This tool runs entirely in your browser using client-side JavaScript. Your usage estimates and calculations are never transmitted anywhere and stay entirely on your device.
How accurate is the LLM pricing data?
Pricing reflects published API rates as of early 2026. LLM providers frequently update their pricing, so always verify current rates on the provider's official pricing page before making budget decisions.
What is the difference between input and output tokens?
Input tokens are the tokens in your prompt — the question, system instructions, and context you send to the model. Output tokens are the tokens the model generates in its response. Models typically charge more for output tokens than input tokens because generation is more compute-intensive.
Which LLM is cheapest for high-volume API usage?
For the lowest per-token cost, open-source models via hosted APIs (Llama 3.1 on Groq or Together.ai) are typically 10–50× cheaper than frontier models like GPT-4o or Claude Opus. Among proprietary models, Gemini 1.5 Flash, GPT-4o-mini, and Claude 3 Haiku offer the best value for high-volume workloads.
What does 'context window' mean for LLMs?
The context window is the maximum number of tokens a model can process in a single request — including both the input prompt and the output response. A larger context window lets you send longer documents, more conversation history, or more complex instructions. Gemini 1.5 Pro has a 2M token context window, while older models like GPT-4 are limited to 128K tokens.
How do I estimate my monthly LLM API cost?
Multiply your daily API calls by the average tokens per call (input + output separately), then multiply by the cost per token for your chosen model, then multiply by 30. Our monthly cost estimator below does this automatically — just enter your daily calls and average token counts.
Is GPT-4o cheaper than Claude 3.5 Sonnet?
GPT-4o costs $2.50/M input tokens and $10.00/M output tokens. Claude 3.5 Sonnet costs $3.00/M input and $15.00/M output. For input-heavy workloads, GPT-4o is slightly cheaper; for output-heavy workloads (long responses), GPT-4o is notably cheaper per token. However, performance differences between models may make one a better value depending on your use case.