How to use the AI Model Comparison Table
1. Use the search box to find models by name, provider, or capability (e.g. "coding", "vision", "multilingual").
2. Filter by provider or toggle to show only open-source models.
3. Click column headers to sort by context window size or pricing.
What is it?
The LLM landscape changes rapidly — new models are released monthly and pricing is constantly updated. This comparison table covers all major production-ready models across OpenAI, Anthropic, Google, Meta, Mistral, and Cohere, giving you a single reference for choosing the right model for your use case.
Key factors to consider: Context window — how much text the model can process at once; Speed — latency for your users; Cost — input and output token pricing; Strengths — what the model excels at; Open source — whether you can self-host to avoid per-token costs.
Prices are list prices as of early 2026. Open-source models (Llama, Mixtral) can be run for free if self-hosted — the listed prices are for managed API access.
Example
Task: High-volume customer support chatbot Requirements: fast responses, low cost, 128k context Budget: < $1 / 1M input tokens
1. GPT-4o mini — $0.15/1M in, very fast 2. Claude 3.5 Haiku — $0.80/1M in, 200k context 3. Gemini 1.5 Flash — $0.075/1M in, 1M context 4. Llama 3.1 8B (Groq) — $0.05/1M in, self-hostable
For high-volume production workloads, cost-per-token is often more important than absolute capability.
Benefits
Generate accurate code from your actual data
TypeScript interfaces, Python dataclasses, and OpenAI function schemas are generated directly from your real JSON — accurate field names, correct types, no guesswork.
Control LLM API costs before you scale
Token counts and per-call cost estimates for GPT-4o, Claude 3.5, and Gemini let you optimise prompt size and model choice before a feature goes to production.
Ready-to-run code and configs, not boilerplate
Every tool produces output you can paste directly into your project — API request code, prompt templates, chunk configurations — adapt rather than write from scratch.
Purpose-built for AI engineering workflows
From RAG chunking strategy to prompt template selection to function calling schema design — tools built for the specific problems AI engineers encounter daily.
Frequently asked questions
Which model is best for coding?
For coding tasks, Claude 3.5 Sonnet and GPT-4o are consistently top performers. o1/o3-mini excel at algorithmic reasoning and competitive programming. For cost-efficient coding, GPT-4o mini or Gemini 2.0 Flash offer strong performance at lower prices.
Which model is best for long documents?
Gemini 1.5 Pro (2M context) is the clear winner for very long documents — entire codebases, legal files, or books. Claude 3.5 Sonnet (200k) is excellent for multi-document analysis. Avoid models with 16k-32k context for long-form tasks.
When should I choose an open-source model?
Open-source models (Llama, Mixtral) are ideal when: data privacy requires on-premise processing, you want to avoid per-token costs at scale, you need fine-tuning control, or GDPR/compliance requires data sovereignty.
What does 'context window' mean in practice?
Context window (in tokens) = how much text the model can read + write in one API call. 1k tokens ≈ 750 words ≈ ½ a page. 128k tokens ≈ a short novel. 2M tokens ≈ a full software repository. Larger context = more you can include, but also more expensive per request.
How often is this table updated?
This comparison reflects model specs as of early 2026. The AI landscape changes rapidly. For the latest models and pricing, check: platform.openai.com, anthropic.com, ai.google.dev, and the respective provider documentation.