An EVREN alternative: the same OpenAI code, a two-line switch to LLMTR

The switch is two lines

EVREN and LLMTR both implement OpenAI's REST contract. In an application written with the official openai SDK, what changes is base_url and api_key; the request body, streaming and error structure stay the same. Model names differ: EVREN uses short names (glm-5.3), LLMTR uses canonical ids with a provider prefix (zai/glm-5.3).

The example below leaves the choice of service to an environment variable. Because the two services name models differently, the model name is chosen together with the service.

Same code, two services

import os
from openai import OpenAI

# Same code, two services. An environment variable picks which one runs.
PROVIDERS = {
    "evren": {
        "base_url": "https://evren-llmapi.ssyz.org.tr/v1",
        "api_key": os.environ.get("EVREN_API_KEY", ""),  # evren_llm_...
        "model": "glm-5.3",
    },
    "llmtr": {
        "base_url": "https://llmtr.com/v1",
        "api_key": os.environ.get("LLMTR_API_KEY", ""),  # llmtr-your_key
        "model": "zai/glm-5.3",
    },
}

cfg = PROVIDERS[os.environ.get("LLM_PROVIDER", "llmtr")]
client = OpenAI(base_url=cfg["base_url"], api_key=cfg["api_key"])

response = client.chat.completions.create(
    model=cfg["model"],
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.model, response.choices[0].message.content)

• Source: EVREN LLM API OpenAPI schema (checked 19 September 2026)

LLMTR counterparts of EVREN's models

The relationship column matters: three rows are the same model, the others are a version from the same family or a different model doing the same job. Do not expect identical results on rows marked 'different model'. The catalog has no direct counterpart for Qwen3-VL-30B.

Prices are the current model prices in the LLMTR catalog and match the provider's price. The 8% platform fee added when topping up credit is not included in these figures.

Same model, different limits

The same model can come with different limits on two services. EVREN states that it serves GLM-5.3 as an FP8-quantised build (zai/glm-5.3-fp8) and gives its context as 512K; the GLM-5.3 row in the LLMTR catalog carries a 1,000,000-token context.

The difference does not always point the same way. For Qwen3-Embedding-8B, the window EVREN states is wider than LLMTR's. The table shows the figure from each side as it is.

When EVREN makes sense

Trying a free service while it is free is the most reasonable thing this article can recommend. If you are a student, run a research project or want to try a model for a short while, EVREN is a good option: setup is short, there is no charge until 1 November and some models are chosen for specific jobs such as Turkish embeddings.

For individual use, e-Devlet login is an advantage too: opening an account takes a few minutes and there is no separate payment step.

• Source: SAYZEK announcement, EVREN LLM Inference Service (checked 19 September 2026)

When LLMTR makes sense

If you use it on behalf of a team or company, the account is opened with an email address and keys belong to the account, not to a person. Each key can get its own spend limit, model allowlist, request rate limit and expiry date.

The price sits in the same place today and after 1 November: every model's price is public in the catalog and on the model page. The catalog serves models from different providers under one key, and every card states where the model is processed and what its provider may record.

• Source: SAYZEK announcement, EVREN LLM Inference Service (checked 19 September 2026)

Using both

You do not have to choose. Because the same code switches between the two services with one environment variable, using both is an option too.

• Trial and evaluate with EVREN during the free period.
• Route production traffic, or everything after the free period, to LLMTR.
• Keep model names in a mapping table; the two services name models differently.
• If you will send sensitive data, read each service's data notes separately.

Frequently asked questions

Do I need to change my code to move from EVREN to LLMTR?

If you use the OpenAI SDK, changing base_url, api_key and the model name is enough. The request body, streaming and error structure stay the same.

Is GLM-5.3 on EVREN the same model as on LLMTR?

It is the same model. EVREN states that it serves an FP8-quantised build and gives the context as 512K; the zai/glm-5.3 row in the LLMTR catalog carries a 1,000,000-token context.

Is LLMTR free?

No, models are charged at the price in the catalog. The catalog also has some models offered free with a daily quota; the model page shows which ones.

What is the 8% platform fee?

It is the platform fee added when topping up credit. It is not added to model prices; models are charged at the provider's price.

Is every EVREN model available on LLMTR?

No. GLM-5.3, Gemma 4 31B and Qwen3-Embedding-8B are the same model; for the others there is a version from the same family or a different model doing the same job. Qwen3-VL-30B has no direct counterpart in the catalog.

Related posts

• What is EVREN? How to use SAYZEK's LLM API
• EVREN quota and credit system: free, but metered
• Migrating to LLMTR with the OpenAI SDK: base_url guide
• OpenRouter alternative for Turkey: how to choose an OpenAI-compatible LLM API

• Model catalog
• Pricing
• API keys
• Python SDK example