Free tool · Picker
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. GPT-6 Astra from OpenAI. OpenAI's most capable: complex reasoning, coding, computer use and research.. Also worth a look: Claude Opus 5.5, Gemini 3.1 Pro.
Top pick
GPT-6 Astra
OpenAIOpenAI's most capable: complex reasoning, coding, computer use and research.
$10 / $50 per 1M (in/out)
Claude Opus 5.5
AnthropicOpus-tier reasoning and agentic coding at 40% of Fable 5.1's per-token price, in and out.
$4 / $20 per 1M (in/out)
Gemini 3.1 Pro
GoogleNative multimodal with a 1M window at a low input price.
$2 / $12 per 1M (in/out)
Prices are USD per 1M tokens, dated in the strip below. Closed-model rates are official standard-tier; open-model rates are representative hosted prices; the weights are free to self-host. GPT-6 Astra, Gemini 3.1 Pro and Grok 4.3 reprice above a long-context threshold; the comparison table carries each one. Confirm live pricing before relying on a figure.
Frequently asked
What's the best AI model for coding in 2026?
For top quality, Claude Fable 5.1 ($10 / $50 per 1M in/out) leads the picker's coding shortlist. For the best value, Grok 4.3 ($1.25 / $2.5 per 1M in/out) is the cheapest managed row on that shortlist. If you want open weights, DeepSeek V4.1 Flash ($0.3 / $1.2 per 1M in/out) is the cheapest capable option on the list and is free to self-host.
Which AI model is cheapest?
Among capable hosted models, Claude Haiku 5.5 ($0.1 / $0.5 per 1M in/out) and GPT-5.4 nano ($0.2 / $1.25 per 1M in/out) are the cheapest rows in the picker, weighted three-to-one toward input price because that is where most bills land. If you self-host open weights instead, token cost is effectively zero and you pay for hardware; the Llama 3.3 70B, Qwen2.5 72B and Mistral Small 3 24B rows are the ones sized for that.
What's the best model I can run fully locally / privately?
For a single consumer GPU, Mistral Small 3 24B is the most practical row here — it is the smallest of them. For more capability on a bigger box, Llama 3.3 70B and Qwen2.5 72B need a high-VRAM card or two. Every one of these is a model the GPU checker can size, so you can price the hardware before you commit to it.
Which model has the biggest context window?
GPT-6 Astra at 1.05M tokens. Claude Fable 5.1, Claude Opus 5.5, Claude Sonnet 5.5, Claude Haiku 5.5, Gemini 3.1 Pro, Gemini 3.8 Flash, Grok 4.3 and DeepSeek V4.1 Flash follow at 1M. For an open, self-hostable flagship, DeepSeek V4.1 Flash at 1M and Mistral Large 3 at 256K.
Open vs. closed models: when should I pick open weights?
Pick open weights when you need data privacy, on-prem or air-gapped deployment, no per-token fees at scale, or full control; DeepSeek V4.1 Flash and Mistral Large 3 are the open rows the picker knows, alongside the smaller Llama 3.3 70B, Qwen2.5 72B and Mistral Small 3 24B you would run yourself. Pick closed when you want the highest ceiling, native vision, managed reliability and zero ops.