AI Model Comparison 2026

Compare ChatGPT, Claude, Gemini, Llama, and Mistral side by side. Pricing, context windows, strengths, and best use cases.

Sort:

Filter:

Provider	Model	Input / 1M tokens	Output / 1M tokens	Context	Key Strengths	Best For
Meta	Llama 3.3 70B Open Source	Free	Free	128K	Fully open-source and free Self-host for full data privacy Strong community and fine-tuning ecosystem	On-premise deployments and custom fine-tuning
Google	Gemini 2.0 Flash	$0.1	$0.4	1M	Ultra-low pricing 1M context window Fastest response times in class	Budget-friendly bulk processing with long context
OpenAI	GPT-4o-mini	$0.15	$0.6	128K	Extremely low cost Fast inference speed Good quality for simple tasks	High-volume, cost-sensitive applications
Anthropic	Claude Haiku 4.5	$0.8	$4	200K	Fast and affordable Good quality at low cost 200K context window	Lightweight tasks needing long context
Google	Gemini 2.0 Pro	$1.25	$5	2M	Massive 2M token context window Strong multimodal capabilities Competitive pricing for its class	Processing very long documents and large codebases
Mistral	Mistral Large	$2	$6	128K	Strong multilingual performance Good reasoning at moderate cost European AI provider (EU data residency)	Multilingual and EU-compliant applications
Anthropic	Claude Sonnet 4.6	$3	$15	200K	Top-tier coding performance Great balance of speed and quality 200K context for large codebases	Coding, software engineering, and technical work
OpenAI	GPT-4o	$5	$15	128K	Strong all-round performance Excellent multimodal (vision, audio) Huge ecosystem and plugin support	General-purpose tasks and multimodal workflows
Anthropic	Claude Opus 4.6	$15	$75	200K	Best-in-class reasoning and analysis Excellent at complex, nuanced writing Strong safety and instruction-following	Complex research, strategy, and deep analysis

Quick Recommendations

Best for coding

Claude Sonnet 4.6

Top-tier code generation, debugging, and refactoring across all major languages.

Best budget option

GPT-4o-mini

Just $0.15 per million input tokens with surprisingly good output quality.

Best for long documents

Gemini 2.0 Pro

2 million token context window processes entire books or large codebases in one go.

Best overall

GPT-4o / Claude Opus 4.6

Both deliver frontier-level reasoning. GPT-4o wins on multimodal breadth; Opus on depth of analysis.

Best open-source

Llama 3.3 70B

Full data privacy via self-hosting, vibrant fine-tuning community, and zero per-token cost.

Disclaimer: Prices and features current as of February 2026. Check provider websites for latest pricing. Actual costs may vary based on usage tier, commitment discounts, and regional availability. Open-source models have no per-token cost but require infrastructure for self-hosting.

How to Choose the Right AI Model in 2026

The AI model landscape has matured significantly. In 2026, businesses and developers have more choice than ever across providers like OpenAI, Anthropic, Google, Meta, and Mistral. The right model depends on your specific use case, budget, and requirements around context length, speed, and data privacy.

Pricing: What Do AI Models Actually Cost?

Most commercial AI models charge per token (roughly 0.75 words). Input tokens (your prompt) are cheaper than output tokens (the model's response). Budget models like GPT-4o-mini and Gemini 2.0 Flash cost under $1 per million input tokens, making them viable for high-volume applications. Frontier models like Claude Opus 4.6 and GPT-4o cost more but deliver superior reasoning for complex tasks.

Context Windows: Why Size Matters

A model's context window determines how much text it can process in a single request. Google's Gemini 2.0 Pro leads with a 2 million token window — enough to process entire books or large codebases. Anthropic's Claude models offer 200K tokens, while OpenAI and others typically provide 128K. Choose a larger context window if you work with long documents, legal contracts, or extensive code repositories.

Open Source vs. Commercial

Meta's Llama 3.3 70B offers a compelling open-source alternative with zero per-token cost. The trade-off is infrastructure: you need to provision and manage GPU servers for self-hosting. This makes open-source models ideal for organisations with strict data residency requirements or those wanting to fine-tune a model on proprietary data.

Which Model Should You Pick?

For most businesses, the answer depends on the task. Use a budget model (GPT-4o-mini, Gemini Flash) for classification, extraction, and simple Q&A. Use a mid-range model (Claude Sonnet, Gemini Pro) for coding, content creation, and analysis. Reserve frontier models (Claude Opus, GPT-4o) for complex reasoning, strategy, and tasks where accuracy is critical.