LLMTR / llmtr/embeddingmagibu-200m
EmbeddingMagibu 200M - access through LLMTR
EmbeddingMagibu 200M is an embedding model trained for Turkish text and hosted in Turkey; it turns text into a normalized 768-dimensional vector for semantic search, RAG, similarity, and clustering. It accepts up to 8,192 tokens per text, and up to 256 texts and 16,384 tokens per request. Send documents with `prompt_name: "document"` and search queries with `prompt_name: "query"`; when comparing two texts, use `"STS"` on both sides. `dimensions` selects 512, 256, or 128 instead of 768. Build an index with one model and the same settings; do not mix it with vectors from another model.
Technical specifications
| Canonical ID | llmtr/embeddingmagibu-200m |
|---|---|
| Provider | LLMTR |
| Context window | 8,192 tokens |
| Operations | EMBEDDINGS |
| Modalities | text, embedding |
Pricing
An 8% platform margin applies to credit top-ups; model usage prices are not separately marked up.
| Operation | Metric | Unit | Price |
|---|---|---|---|
| EMBEDDINGS | INPUT_TEXT | PER_1M_TOKENS | $0.010000 |
Example usage
With existing OpenAI SDK flows, change only the base URL and model identifier.
curl https://llmtr.com/v1/chat/completions -H "Authorization: Bearer llmtr-your_key" -H "Content-Type: application/json" -d '{"model":"llmtr/embeddingmagibu-200m","messages":[{"role":"user","content":"Hello"}]}'Guides about this model
- 400 Bad Request: embeddinggemma Does Not Support Chat - EmbeddingGemma returns numeric vectors rather than generated text. Route Ollama requests to POST /api/embed and LLMTR requests to POST https://llmtr.com/v1/embeddings with llmtr/embeddingmagibu-200m, the successor of the retired llmtr/embeddinggemma-300m. On LLMTR, a chat endpoint returns HTTP 400 unsupported_operation for this model.
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