LLM
Introduction
The connection to an LLM is based on
- An API protocol (OpenAI or specific)
- A URL endpoint, as shown below
- An API token the customer must enter when creating the configuration.
For example, to obtain your key from ChatGPT, please refer to the following page:
Example of LLM Types and Models
| LLM | API Endpoint URL | Model Examples |
|---|---|---|
| ChatGPT (OpenAI) | https://api.openai.com/v1/chat/completions |
gpt-5.5, gpt-5.4-mini, o3 |
| Claude (Claude) | https://api.anthropic.com/v1/messages |
claude-opus-4-8, claude-sonnet-4-6, claude-haiku-4-5 |
| DeepSeek (OpenAI) | https://api.deepseek.com/v1/chat/completions |
deepseek-v4-pro, deepseek-v4-flash, deepseek-r1 |
| Fireworks (OpenAI) | https://api.fireworks.ai/inference/v1/chat/completions |
accounts/fireworks/models/llama-v3p3-70b-instruct, accounts/fireworks/models/deepseek-v3 |
| Gemini (Gemini) | https://generativelanguage.googleapis.com/v1beta/models/ |
gemini-3.1-pro, gemini-3.5-flash, gemini-2.5-flash-lite |
| Groq (OpenAI) | https://api.groq.com/openai/v1/chat/completions |
llama-3.3-70b-versatile, gpt-oss-120b |
| Mistral (OpenAI) | https://api.mistral.ai/v1/chat/completions |
mistral-large-3, mistral-small-4, codestral |
| OpenRouter (OpenAI) | https://openrouter.ai/api/v1/chat/completions |
openai/gpt-5.5, anthropic/claude-sonnet-4-6, google/gemini-3.1-pro |
| Perplexity (OpenAI) | https://api.perplexity.ai/chat/completions |
sonar-pro, sonar |
| Together AI (OpenAI) | https://api.together.xyz/v1/chat/completions |
meta-llama/Llama-3.3-70B-Instruct-Turbo, deepseek-ai/DeepSeek-V4-Pro |
| xAI Grok (OpenAI) | https://api.x.ai/v1/chat/completions |
grok-4, grok-4-fast, grok-3-mini |
Note: European customers may lean towards Mistral.
The LLM models change often. The above are examples as of June 2026.
LLM Creation Information
Below is a view of the Create page.
Pick OpenAI for all LLM whose API is compatible, that's all the LLM but Claude and Gemini. In the example below, Mistral uses the OpenAI protocol; Please note the Mistral URL.
Token Count
When viewing the LLM connection, the number of tokens used up to that point will be displayed.
When an LLM is used for translation, the number of tokens will be shown in the log file for that translation. The total tokens used for the project will be tracked on the Localyzer page.
Prompting
For best results with an LLM, prompting is recommended. Localyzer allows for three levels of prompting:
See: Prompting
Context Prompting
If the LLM connection is configured with an InContext server, then when a context has been associated with a string (See String Manager / Context), the context will be passed to ChatGPT to provide the necessary information to better translate that string.
Make sure the LLM model takes advantage of the InContext image. Not all models do.