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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-04-28 · Source: legacy_model_catalog
Mixed text, image, audio and document workloads that benefit from one model across modalities.
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
gpt-4.1-nano is a Multimodal model from OpenAI (US). HotON.ai tracks it at $0.10 per 1M input tokens and $0.40 per 1M output tokens, with a 1048K-token context window. Its composite efficiency score is 96/100 at an estimated $0.000 per successful task.
gpt-4.1-nano is tracked at $0.10 per 1M input tokens and $0.40 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.33 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
gpt-4.1-nano supports up to a 1048K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, gpt-4.1-nano is cheaper than 79% of models on input price and ranks #17 of 535 by overall efficiency.
Yes — qwen3.5-flash-02-23 is a lower-cost option at $0.26 per 1M output tokens, while still covering similar Multimodal use cases. Compare them side by side on HotON.ai.
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HotON.ai — gpt-4.1-nano (OpenAI): $0.10/1M input, $0.40/1M output, as of 2026-04-28. https://hoton.ai/en/models/openai-gpt-4-1-nanoPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.