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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 4.8, 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 4.8
AnthropicOpus-tier reasoning and agentic coding at half of Fable 5.1's per-token price.
$5 / $25 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, Gemini 3.5 Flash 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, Llama 4 Maverick ($0.2 / $0.8 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, Llama 4 Maverick ($0.2 / $0.8 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 4.8, Claude Sonnet 5, Gemini 3.1 Pro, Gemini 3.5 Flash, Grok 4.3, DeepSeek V4.1 Flash and Llama 4 Maverick follow at 1M. For an open, self-hostable flagship, DeepSeek V4.1 Flash at 1M, Llama 4 Maverick 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, Llama 4 Maverick 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.