Compare top AI models side-by-side — pricing, performance, features & more (Updated March 2026)
| Model | Provider | Input $/1M | Output $/1M | Context | Speed (tok/s) | Coding | Reasoning | Multimodal | Open Source | Try It |
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The AI landscape has become increasingly competitive, with major models from OpenAI, Anthropic, Google, Meta, and emerging players like DeepSeek all vying for dominance. Choosing the right AI model depends on your specific use case — coding assistance, creative writing, data analysis, or general conversation — and your budget. Our comparison tool lets you evaluate models across the metrics that matter most: pricing, context window size, speed, and capability benchmarks.
Pricing structure varies significantly between models. Most providers charge per token (roughly 3/4 of a word), with separate rates for input and output tokens. Output tokens are typically 3-5x more expensive than input tokens because they require more computation. For high-volume applications, the difference between GPT-4o at $2.50/$10 per million tokens and Claude 3.5 Sonnet at $3/$15 per million tokens can translate to hundreds of dollars monthly.
Context window determines how much information the model can process at once. Gemini 1.5 Pro leads with up to 2 million tokens, enabling analysis of entire codebases or lengthy documents. Claude offers 200K tokens, while GPT-4o provides 128K. For most use cases, 128K tokens is sufficient — that is roughly equivalent to a 300-page book.
Speed and latency matter for interactive applications. Smaller models like GPT-4o Mini and Claude 3.5 Haiku deliver responses in under a second, while larger reasoning models can take 10-30 seconds for complex problems. If you need fast responses for a chatbot, prioritize speed. For complex analysis where accuracy matters more, a slower reasoning model is worth the wait.
For coding: Claude Opus and GPT-4o consistently score highest on coding benchmarks like HumanEval and SWE-Bench. Claude excels at understanding large codebases thanks to its 200K context window, while GPT-4o offers faster iteration speed. DeepSeek R1 has emerged as a strong open-source alternative for coding tasks.
For creative writing: Claude models tend to produce more nuanced, natural-sounding prose. GPT-4o is strong for structured content. Gemini Ultra handles multilingual creative tasks well.
For data analysis: Models with large context windows (Gemini, Claude) excel when you need to process large datasets. GPT-4o's Code Interpreter feature adds the ability to run Python code, making it particularly powerful for data analysis workflows.
For most tasks, GPT-4o Mini and Claude 3.5 Haiku offer the best value at a fraction of the cost of larger models. Open-source models like Llama 3.3 (70B) and DeepSeek R1 can be run locally or through providers at even lower costs. The cheapest option depends on your volume — at low volumes, even premium models cost pennies per query.
The gap has narrowed significantly. Llama 3.3 and DeepSeek R1 approach or match proprietary models on many benchmarks. For specialized tasks with fine-tuning, open-source models can exceed proprietary ones. The main advantages of proprietary models are convenience, safety features, and performance on the most demanding reasoning tasks.
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