An LLM model comparison chart helps you evaluate and choose the right large language model for your needs. With dozens of AI models available from OpenAI, Anthropic, Google, Meta, Mistral, and DeepSeek, comparing pricing, context windows, capabilities, and features side by side saves hours of research. Use the filters below to narrow down models by use case, budget, and requirements.
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About This Comparison
Pricing data is approximate and reflects publicly available API rates as of early 2026. Actual costs may vary based on usage tier, commitments, and provider promotions. Open-source model pricing shown is for hosted API access; self-hosting costs vary. Always verify with the provider's official pricing page before making decisions.
How to Use the LLM Model Comparison Chart
Choosing the right large language model can be overwhelming with so many options available. This LLM comparison chart lets you quickly evaluate models across the dimensions that matter most: pricing, context window size, supported use cases, and whether the model is open source or multimodal. Instead of visiting each provider's website individually, compare everything in one interactive table.
Step 1: Search or Filter by Name
If you already know which models you want to compare, type a model name or provider into the search box. The table filters in real time, showing only matching models. For example, typing "Claude" shows all Anthropic models, while typing "Llama" shows Meta's open-source offerings.
Step 2: Filter by Use Case
Use the "Use Case" dropdown to narrow models by what they excel at. Select "Coding" to see models best suited for code generation and debugging, "Writing" for content creation, "Analysis" for data and research tasks, or "Chat" for conversational applications. Each model is tagged with its strongest use cases based on benchmarks and community consensus.
Step 3: Set Your Budget
The "Price Range" filter groups models by their input token cost. If you are building a high-volume application, filtering for "Free / Very Cheap" or "Cheap" models can help you find cost-effective options. For tasks requiring maximum quality regardless of cost, "Premium" models like Claude Opus 4 offer the highest capability.
Step 4: Sort and Compare
Click any column header to sort the table. Sort by "Input $/1M" or "Output $/1M" to rank models by cost, by "Context" to find models that can handle the largest documents, or by "Model" name for alphabetical browsing. Click the same header again to reverse the sort order. This makes it easy to find the cheapest model with a specific context window, or the most capable model within your budget.
Step 5: Check Features
Use the toggle filters to show only open-source models (which you can self-host for more control) or multimodal models (which can process images in addition to text). These feature filters combine with all other filters, so you can find, for example, the cheapest open-source model with a 128K+ context window that is good for coding.
Tips for Choosing an LLM
Start with the cheapest model that meets your quality bar, then upgrade only if needed. For prototyping, use affordable models like GPT-4o-mini or Gemini 2.5 Flash. For production workloads requiring top-tier reasoning, Claude Opus 4 or GPT-4o are strong choices. Consider open-source models like Llama 3.1 405B or DeepSeek V3 if you need full control over deployment, data privacy, or want to avoid per-token API costs at scale.
FAQ
Is this LLM comparison tool free?
Yes, this tool is completely free with no signup, no API keys, and no hidden fees. Everything runs locally in your browser. You can filter, sort, and compare models as much as you need.
Is my data safe when using this tool?
Absolutely. This tool runs entirely in your browser using static data and JavaScript. No information is sent to any server, stored, or tracked. Your browsing is completely private.
How often is the pricing data updated?
Pricing data is approximate and reflects publicly available rates as of early 2026. AI model pricing changes frequently, so always verify with the provider's official pricing page before making purchasing decisions.
Which LLM is best for coding tasks?
For coding, Claude Opus 4, GPT-4o, and Claude Sonnet 4 are top choices. Claude Opus 4 excels at complex multi-file refactoring, while GPT-4o and Claude Sonnet 4 offer a good balance of code quality and cost. For budget-friendly coding, DeepSeek V3 and Llama 3.1 405B are strong open-source options.
What is a context window in LLM models?
A context window is the maximum amount of text (measured in tokens) that a model can process in a single conversation. Larger context windows let you include more documents, code, or conversation history. For example, Gemini 2.5 Pro offers 1M tokens, while most models offer 128K-200K tokens.
What is the cheapest LLM model available?
Among the models listed, Gemini 2.5 Flash and GPT-4o-mini offer the lowest per-token pricing. Open-source models like Llama and DeepSeek V3 can be even cheaper when self-hosted, though hosting costs vary. The best value depends on your specific quality requirements.
What is the difference between open source and closed source LLMs?
Open-source LLMs like Llama and Mistral publish their model weights, allowing anyone to download, modify, and self-host them. Closed-source models like GPT-4o and Claude are only accessible through the provider's API. Open-source models offer more control and potentially lower costs at scale, while closed-source models often lead in capability.
Can I compare custom or fine-tuned models here?
This tool covers the most popular foundation models from major providers. Fine-tuned or custom models are not included since their pricing and capabilities vary by deployment. However, you can use the base model comparison as a starting point for evaluating fine-tuned variants.