Free tool
Which AI Model Should I Use?
Tell us your use case and what matters most — quality, cost, or speed — and get a clear top pick plus two alternatives, with the reasoning and ballpark pricing. No signup.
Top pick
Claude Opus 4.8
AnthropicAnthropic's most capable — top-tier coding, long-horizon agents, 1M context.
$5 / $25 per 1M (in/out)
Claude Sonnet 4.6
AnthropicBest balance of speed, cost and intelligence; same 1M context at ~60% of Opus.
$3 / $15 per 1M (in/out)
GPT-4o
OpenAIWell-rounded multimodal workhorse; native vision and broad tooling.
$2.5 / $10 per 1M (in/out)
Prices are a June 2026 snapshot (USD per 1M tokens). Closed-model rates are official standard-tier; open-model rates are representative hosted prices — the weights are free to self-host. Gemini 2.5 Pro rises to $2.50/$15 over 200K-token prompts. Confirm live pricing before relying on a figure.
Frequently asked
What's the best AI model for coding in 2026?
For top quality, Claude Opus 4.8 ($5 / $25 per 1M in/out, 1M context) leads on agentic coding. For the best value, Claude Sonnet 4.6 ($3 / $15) or the open DeepSeek V3 (~$0.25 / $0.34 hosted, free to self-host) deliver frontier-class coding for far less.
Which AI model is cheapest?
Among capable hosted models, Mistral Small 3.x ($0.10 / $0.30 per 1M) and GPT-4o mini ($0.15 / $0.60) are the cheapest, with Gemini 2.5 Flash ($0.30 / $2.50) cheap for huge contexts. If you self-host open weights (Llama, Qwen, DeepSeek, Mistral), token cost is effectively zero — you only pay for hardware.
What's the best model I can run fully locally / privately?
For a single consumer GPU, Mistral Small 3.x (~24B, Apache 2.0) is the most practical. For frontier-class quality on a multi-GPU box, DeepSeek V3, Llama 4 Maverick, or Mistral Large 3 (all open weights) are the strongest self-hostable options. Mid-range: Llama 3.3 70B or Qwen2.5 72B fit a single high-VRAM GPU.
Which model has the biggest context window?
Claude Opus 4.8, Claude Sonnet 4.6, Gemini 2.5 Pro/Flash, and the open Llama 4 Maverick all offer a 1,000,000-token context. For an open, self-hostable flagship, Mistral Large 3 offers 256K+.
Open vs. closed models — when should I pick open weights?
Pick open weights (Llama, Qwen2.5, DeepSeek, Mistral) when you need data privacy, on-prem/air-gapped deployment, no per-token fees at scale, or full control. Pick closed (Claude, GPT-4o, Gemini) when you want the highest ceiling, native vision, managed reliability, and zero ops.