Addis AI Pricing

Powerful local language AI models at competitive prices

Addis-፩-አሌፍ NLP

Flagship model for Ethiopian language text generation and chat.

AmharicAfan Oromo
ETB 0.30per 1,000 input tokens
ETB 0.80per 1,000 output tokens
Best for Chatbots & Content

አሌፍ-Audio-AM Amharic Audio

Amharic text-to-speech for voice apps and accessibility.

Amharic
ETB 1per minute
Best for Amharic Voice Apps

አሌፍ-Audio-OM Afan Oromo Audio

Afan Oromo text-to-speech for Oromo voice applications.

Afan Oromo
ETB 5per minute
Best for Oromiffa Voice Apps

አሌፍ-1.2-realtime-audio Realtime Audio

Advanced audio model with natural, native-like audio input and output, and a response time of <300ms. Suitable for natural, fast audio interactions.

Amharic
ETB 1per 1,000 input tokens
ETB 4per 1,000 output tokens
~ ETB 13/ minute (estimated)
Best for Live Audio Apps
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How Token Pricing Works

Tokens are the basic units of text processed by our models. Pricing is based on the number of tokens in your input (prompt) and the model's output (response). Here's a practical guide:

In Ge'ez (Amharic), tokens often map to 1-2 characters.

Example:

ሰላም! እንደምን ነህ?

≈ 9 Tokens

In Latin scripts (like Afan Oromo), tokens often map to syllables or whole words.

Example:

Akkam jirta?

≈ 4 Tokens

Real-World Examples

Article Summarization (Addis-፩-አሌፍ)

Summarize a 500-word news article into three concise bullet points in Amharic.

~ETB 0.52

(Based on a ~400 token prompt and ~150 token response)

Podcast Intro (አሌፍ-Audio-AM)

Convert a 30-second introductory script (about 75 words) into a high-quality audio file.

ETB 0.50

(Based on a 0.5 minute audio length)

Voice Message (አሌፍ-Audio-OM)

Generate a 2-minute voice message in Afan Oromo from a provided text script.

ETB 10.00

(Based on a 2 minute audio length)

Live Customer Support

A 1-minute live conversation with a customer, involving both understanding their speech and responding.

~ETB 4.50

(Approx. 1k STT tokens and 1k TTS tokens)

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Model Selection

In most API requests, the appropriate model is automatically selected based on:

  • The language or target_language parameter (am or om)
  • The endpoint you're using (/chat_generate or /audio)

You typically don't need to specify which model to use - the API will select the appropriate model based on the task and language.