Compare 18+ large language models side-by-side: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, Cohere, Grok, DeepSeek. Sort by price, context window or release date. Filter by provider or capability (vision, function calling). Pick the right model in 60 seconds. Updated regularly, free, no signup.
| Model | Context | Input $/1M | Output $/1M | MMLU | Capabilities | Released |
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There is no single "best" large language model — the right choice depends entirely on what you're building and what you're willing to pay. This AI model comparison table puts the major LLMs side-by-side across the four metrics that actually matter for most decisions: context window size, input and output token pricing, capabilities (vision, function calling, open source) and benchmark performance. Filter, sort and search until you find the model that fits your budget and your requirements.
Most teams pick GPT-4o or Claude 3.5 Sonnet as a starting "good general default", then drop down to cheaper models (GPT-4o mini, Claude Haiku, Gemini Flash) for high-volume sub-tasks where quality requirements are lower. For very long documents, Gemini 1.5 Pro's 2M-token context window is unmatched. For 100% on-premise or offline inference, Llama 3, Mistral and DeepSeek are open-source options. For cutting-edge frontier capability and reasoning, GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro currently lead public benchmarks.
The maximum number of tokens (input + output combined) the model can process in a single request. A 128K-token window holds roughly 100,000 English words — about a 250-page book. Larger context = more documents you can stuff in for retrieval-augmented generation (RAG), longer conversation history you can maintain, more code you can review at once. Gemini 1.5 Pro currently leads at 2M tokens; Claude is at 200K; GPT-4o is at 128K.
API providers charge per million tokens, with output tokens typically 3–5× more expensive than input. A typical cost ratio: input $0.50/M, output $1.50/M for a budget model; input $15/M, output $75/M for a premium model. For most chatbot use cases, input tokens dominate (the model sees long context, returns short answers) so input price matters most. For long-form generation use cases (article writers, code generators), output price is the bigger driver.
"Vision" means the model can accept images as input — useful for OCR, chart interpretation, screenshot analysis. "Function calling" (or "tool use") means the model can output structured JSON that triggers a function in your app — essential for building agents, RAG systems, and complex workflows. Almost all major closed-source frontier models now support both; open-source models vary.
The Massive Multitask Language Understanding benchmark measures factual knowledge and reasoning across 57 subjects (math, history, biology, law, etc.). It's the most-cited single-number benchmark for general LLM capability. As of mid-2026, frontier models score 85–92; mid-tier models 75–85; lighter models 65–75. MMLU is not a perfect predictor of real-world quality but it correlates well.
The answer depends on the task:
Use the search field to find a model by name or provider. Use the provider chips to limit the table to one company at a time. Use the capability filter to show only models that support vision, function calling or are open source. Click any column header to sort ascending or descending. The cheapest row is highlighted green; the row with the biggest context window is highlighted in the primary color.
There is no single winner. For frontier capability, GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro are roughly equivalent and trade leadership across different benchmarks. For best price/performance, GPT-4o mini, Claude 3 Haiku and Gemini 1.5 Flash dominate the budget tier. For open-source self-hosting, Llama 3.1 70B and DeepSeek V2 are top choices. Use the table above to filter by your priority (price, context, capability).
The context window is the maximum number of tokens (input + output combined) the model can handle in one request. 128K tokens is about 100,000 English words, or roughly a 250-page book. Larger context = more documents for RAG, longer conversation memory, more code reviewed at once. Gemini 1.5 Pro leads at 2M tokens.
Generation is computationally more expensive than reading. Each output token requires a full forward pass through the model with attention over all prior tokens. Pricing reflects this: output tokens cost 3–5× more than input tokens across most providers. For high-volume use, input-heavy use cases are dramatically cheaper than long-form generation use cases.
MMLU (Massive Multitask Language Understanding) tests factual knowledge and reasoning across 57 academic subjects. It's the most-cited single-number benchmark for general LLM capability. Reliable as a rough indicator but should be paired with task-specific benchmarks (HumanEval for code, GPQA for science, MATH for math, etc.) before making a final choice.
Use open-source if: you need on-prem deployment for privacy or compliance, your token volume is so high that closed-API costs are prohibitive, or you need to fine-tune on proprietary data. Otherwise, the engineering overhead of self-hosting (GPU costs, deployment, monitoring) usually outweighs the per-token savings vs hosted alternatives like Together.ai or Groq.
Function calling (or "tool use") lets the model output structured JSON that maps to a function in your app. Instead of free-text "I think you should query the database for X", the model outputs {"function": "query_db", "args": {"table": "X"}} which your code can execute. Essential for building agents, RAG systems, and any app where the LLM needs to take actions in the real world.
Prices, context windows and benchmarks are reviewed regularly and updated as providers announce changes. The "Prices updated" date below the table reflects the last refresh. For mission-critical pricing decisions, always verify directly on the provider's pricing page before committing.
We focus on production-ready, generally-available models from major providers. Specialty models (code-only models, fine-tuned variants, beta releases) are intentionally excluded to keep the comparison fair. If a major model is missing or a price has changed, let us know via the contact page.