API Documentation

Authentication

Authorization: Bearer sk-st-your-key-here

Base URL

https://api.staik.se/v1

Chat Completions

curl https://api.staik.se/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-st-your-key" \
  -d '{
    "model": "gemma4:31b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Audio transcription

Transcribe audio to text via KB-Whisper large — the Swedish National Library's Swedish-trained Whisper model (50,000 hours of Swedish speech). OpenAI-compatible — just point the openai SDK at api.staik.se.

curl https://api.staik.se/v1/audio/transcriptions \
  -H "Authorization: Bearer sk-st-your-key" \
  -F file=@moten.mp3 \
  -F model=kb-whisper-large \
  -F language=sv

Python (OpenAI SDK drop-in):

from openai import OpenAI
client = OpenAI(api_key="sk-st-your-key", base_url="https://api.staik.se/v1")

with open("moten.mp3", "rb") as f:
    result = client.audio.transcriptions.create(
        model="kb-whisper-large",  # eller "whisper-1" — båda routar till KB-Whisper large
        file=f,
        language="sv",
    )
print(result.text)

Token consumption

Audio transcription is billed at 100 tokens per second of audio from the same token pool as the chat models. That means 1 minute of audio costs 6,000 tokens and 1 hour costs 360,000 tokens.

Every response includes headers X-Audio-Duration-Seconds and X-Audio-Tokens-Charged so you can track usage per request. Max file size: 25 MB (~30–45 min of audio). Supported formats: mp3, wav, m4a, ogg, flac, webm, mp4. response_format: json (default), text, or verbose_json (with segments).

Models

Choose model via the model field in your request. Each model runs on dedicated GPU hardware in Sweden.

Chat models support streaming (SSE). Context windows: gemma4:31b 96K, qwen3.6:35b-a3b 262K, qwen3.5:9b 262K. Only gemma4:31b has built-in vision, send images via image_url in messages. bge-m3:latest is an embedding model returning 1024-dimensional vectors (configure pgvector as vector(1024)), called via POST /v1/embeddings. kb-whisper-large is KB's Swedish-trained Whisper model, called via POST /v1/audio/transcriptions.

The models are open weights that we host on our own hardware in Sweden. Training data and responses are determined by the model maker, not by staik. Choose the model in the model field based on your use case. KB-Whisper is the only model we run that is trained in Sweden.

Plans

Error codes

Rate limit headers

Every response includes headers showing your current token usage.