Interactive tool to compare 863+ AI models side by side: price per token, speed, benchmarks and context window. Find which model is best for your use case in 2026.
By Luis Fernando Roquette • Last updated: October 02, 2026 •863 models available
Select two models to see a detailed side-by-side comparison.
Data from ELO Chatbot Arena, Artificial Analysis and OpenRouter. ELO: daily • Prices: weekly.
| Model | ELO | Intel. | Code | $/1M in | $/1M out | tok/s | Context | Multi | OSS |
|---|---|---|---|---|---|---|---|---|---|
| — | 57.6 | — | $4.00 | $20.00 | 93 | 1.0M | — | — | |
| — | 56 | — | $4.00 | $20.00 | 83 | — | — | — | |
| — | 56 | — | $2.00 | $10.00 | 139 | 1.0M | — | — | |
| — | 53.6 | — | $4.00 | $20.00 | 75 | — | — | — | |
| 1,501 | 53.4 | 81.6 | $10.00 | $50.00 | 68 | 1.0M | — | — | |
| — | 53.2 | 80.7 | $10.00 | $50.00 | 64 | — | — | — | |
| 1,476 | 52.7 | 76.9 | $10.00 | $50.00 | 51 | 1.1M | — | — | |
| 1,525 | 52.6 | — | $2.00 | $10.00 | — | — | — | — | |
| — | 52.4 | 75.9 | $10.00 | $50.00 | 49 | — | — | — | |
| — | 51.9 | — | $2.00 | $10.00 | 103 | — | — | — |
Intel. = Intelligence Index (0–100) · Code = Coding Index · tok/s = tokens per second · Multi = multimodal · OSS = open source. See full methodology →
Comparing AI models requires multidimensional analysis. There is no single “best model” — the choice depends on the use case, budget, and technical requirements. The key criteria are: response quality (measured by benchmarks like MMLU and GPQA), cost per token, inference speed, context window size, tool calling support, multimodality, and language-specific performance.
AI models are generally charged per “token” — units of processed text. One token is roughly 3/4 of a word in English. Pricing varies dramatically: from $0.01/1M tokens (lightweight models) to $60+/1M tokens (frontier models). For high-volume applications like customer support chatbots, the cost difference can add up to thousands of dollars per month.
The context window determines how much text the model can “see” at once. Models with a small context window (8K–32K tokens) are suited for simple queries and short conversations. Models with large context (128K–200K) process entire documents, contracts, and codebases. The Gemini Pro line has offered the largest windows (1M+ tokens) — enough for entire books.
For real-time applications (chatbots, code autocomplete), generation speed (tokens per second) and initial latency (time to first token) are crucial. Smaller models (GPT mini variants, Claude Haiku, Mistral Small) are significantly faster than frontier models. Latency also varies by region — consider your proximity to the provider’s data centers when evaluating performance.
MMLU (Massive Multitask Language Understanding) tests general knowledge across 57 disciplines. GPQA Diamond tests reasoning in physics, chemistry, and biology at PhD level. SWE-bench tests real-world code bug resolution. Chatbot Arena (LMSYS) measures human preference in conversations. No single benchmark tells the full story — use multiple for a balanced view.
The most popular comparisons include: ChatGPT vs Claude (the two most widely used models), Gemini vs ChatGPT (Google vs OpenAI ecosystem), Claude vs GPT for code (which is better for programming), and open source vs proprietary models (Llama vs GPT — when to use each). Use the tool above to compare any combination of models.
A proper comparison should consider multiple factors: quality benchmarks (MMLU, GPQA), price per token, inference speed, context window size, tool calling support, multimodality, and performance on your specific task. There is no universal "best" — it depends on your use case.
GPT (OpenAI) and Claude (Anthropic) are the two most popular frontier models. GPT tends to be more versatile and integrated (ChatGPT, Copilot). Claude excels at following complex instructions, long contexts (200K tokens), and safety. Both deliver strong performance across English and other languages.
The latest ChatGPT and Claude Opus generations compete at the top of the rankings. ChatGPT tends to be faster at generation. Claude Opus is more precise for reasoning and long-form analysis. For coding, both are excellent. For cost-efficiency at high volume, the lighter tiers of each family (mini/flash, Haiku) are recommended.
Gemini (Google) has advantages in context window (often 1M+ tokens), Google Search integration, and native multimodal processing. ChatGPT has advantages in ecosystem (plugins, GPT Store) and speed. For general-purpose use, both are highly competitive — check current scores at /benchmark.
The lightest tier of each major family (OpenAI, Anthropic, DeepSeek) typically offers excellent quality for less than $0.30/1M tokens. For free local use, open source models like Llama and Qwen can be run via Ollama at zero API cost.