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Real human-preference Elo from LMArena blind head-to-head votes. Higher is better; — means not yet ranked in that arena. This is measured, not our estimate.
Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Source: litellm
Mixed text, image, audio and document workloads that benefit from one model across modalities.
Azure OpenAI deployment reference for GPT-5.1 Chat preview; actual deployment name, API version, and region are customer-specific.
gpt-5.1-chat is a Multimodal model from Azure OpenAI (US). HotON.ai tracks it at $1.38 per 1M input tokens and $11.00 per 1M output tokens, with a 128K-token context window. Its composite efficiency score is 84/100 at an estimated $0.008 per successful task.
gpt-5.1-chat is tracked at $1.38 per 1M input tokens and $11.00 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $8.60 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
gpt-5.1-chat supports up to a 128K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, gpt-5.1-chat is cheaper than 22% of models on input price and ranks #480 of 535 by overall efficiency.
Yes — grok-4-fast is a lower-cost option at $0.50 per 1M output tokens, while still covering similar Multimodal use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — gpt-5.1-chat (Azure OpenAI): $1.38/1M input, $11.00/1M output. https://hoton.ai/en/models/azure-openai-gpt-5-1-chatPricing 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.