LLMTR / llmtr/trendyol-asure-12b
Trendyol Asure 12B - access through LLMTR
Trendyol Asure 12B is a 12B chat model produced by Trendyol and hosted in Turkey. With a 40,960-token context window it fits Turkish chat, RAG chatbots, customer-support assistants, e-commerce knowledge flows, summarization, product descriptions, and category or tagging work. It can read images: image input is sent as a base64 data URL, and remote image URLs are not accepted. Repeated prompt prefixes are served from cache and billed at the lower cache rate. Audio and video input, image generation, and tool calling are not supported.
Technical specifications
| Canonical ID | llmtr/trendyol-asure-12b |
|---|---|
| Provider | LLMTR |
| Context window | 40,960 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 | $0.100000 |
| CHAT_COMPLETIONS | CACHE_READ | PER_1M_TOKENS | $0.025000 |
| CHAT_COMPLETIONS | OUTPUT_TEXT | PER_1M_TOKENS | $0.500000 |
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/trendyol-asure-12b","messages":[{"role":"user","content":"Hello"}]}'Guides about this model
- The LLMTR provider block in OpenCode configuration and model naming - In OpenCode the first-party llmtr routes arrive preloaded, while every other catalog identifier is added to the configuration file by hand. This guide covers the connect flow, the shape of the provider block and model naming.
- OpenClaw LLMTR plugin: package install and manifest walkthrough - OpenClaw does not bake the LLMTR provider into its core. It carries the integration as a package under the extensions directory with its own manifest and model catalog, and this guide unpacks both.
- 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.
- DeepSeek Harness LLMTR plugin: the LLM seam and catalogue - In DeepSeek Harness a provider integration is not written into the core; it arrives as a plugin attached to the ctx.llm seam. The model list is not bundled either, it is read from the endpoint.
- Trae Agent with LLMTR: Python configuration and trajectory records - Trae Agent reaches LLMTR through a dispatching class that splits models across two client implementations. This guide walks the Python configuration, the routing decision and the fields visible in the run record.
- 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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