LLMTR / llmtr/muse-glimmer-30b-tr
Muse Glimmer 30B (Turkey) - access through LLMTR
LLMTR Muse Glimmer 30B is our Turkey-hosted deployment of Meta's Apache 2.0 open-weight model. It is invoked through the OpenAI-compatible Chat Completions API and requests are never forwarded to a third-party provider. It offers a 131,072-token context window (prompt and completion together, per request). It makes tool/function calls and reads images: image input is sent as a base64 data URL, and remote image addresses are not accepted. The model reasons step by step before every answer; that reasoning is returned to you in `reasoning_content`, is spent from your `max_tokens` budget, and cannot be turned off. Repeated prompt prefixes are served from cache and billed at a discounted rate. Sending an identical request again within a short window may return the previous answer verbatim. Audio and video input are not supported.
Technical specifications
| Canonical ID | llmtr/muse-glimmer-30b-tr |
|---|---|
| Provider | LLMTR |
| Context window | 131,072 tokens |
| Operations | CHAT_COMPLETIONS |
| Modalities | text, image |
Pricing
An 8% platform margin applies to credit top-ups; model usage prices are not separately marked up.
| Operation | Metric | Unit | Price |
|---|---|---|---|
| CHAT_COMPLETIONS | INPUT_TEXT | PER_1M_TOKENS | $2.00 |
| CHAT_COMPLETIONS | CACHE_READ | PER_1M_TOKENS | $0.500000 |
| CHAT_COMPLETIONS | OUTPUT_TEXT | PER_1M_TOKENS | $5.00 |
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/muse-glimmer-30b-tr","messages":[{"role":"user","content":"Hello"}]}'Guides about this model
- Adding the LLMTR gateway entry to OpenClaude and how discovery works - OpenClaude defines LLMTR as its own gateway record with a dedicated base URL, a dedicated credential variable and a hybrid catalog fed by seed entries. This guide walks that record field by field and explains how the model list fills up.
- Strix LLMTR setup: model prefix, dedupe and the reporting chain - Built from the real files on two separate Strix branches, this guide walks through model prefix resolution, why the dedupe model needs its own endpoint, and which Turkey-hosted rows actually support the tool calls an agent loop needs.
- The models.dev LLMTR provider entry and metadata sync - models.dev is not a service but an open metadata database holding model ids and capabilities in TOML files. We compare the current LLMTR entry against the live catalog to see which rows are missing and which one is retired.
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