OpenAI API Migration Guide - Telnyx

Swap two environment variables and change the model name. That’s it.

export OPENAI_BASE_URL='https://api.telnyx.com/v2/ai/openai'
export OPENAI_API_KEY='KEY***'

from openai import OpenAI

client = OpenAI()  # picks up env vars
chat_completion = client.chat.completions.create(
    model="zai-org/GLM-5.3-Flash",
    messages=[{"role": "user", "content": "Tell me about Telnyx"}],
    reasoning_effort="high",
    temperature=0.0,
    stream=True,
)

Or pass explicitly:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("TELNYX_API_KEY"),
    base_url="https://api.telnyx.com/v2/ai/openai",
)
chat_completion = client.chat.completions.create(
    model="zai-org/GLM-5.3-Flash",
    messages=[{"role": "user", "content": "Tell me about Telnyx"}],
    reasoning_effort="high",
    temperature=0.0,
    stream=True,
)

Reasoning models

Reasoning models such as zai-org/GLM-5.3-Flash add a reasoning_content field alongside the usual content. It holds the model’s chain-of-thought and appears on message (non-streaming) or delta (streaming). Read it the same way you read content:

chat_completion = client.chat.completions.create(
    model="zai-org/GLM-5.3-Flash",
    messages=[{"role": "user", "content": "Tell me about Telnyx"}],
    reasoning_effort="high",
)
message = chat_completion.choices[0].message

# Reasoning models populate reasoning_content; other models leave it None.
if getattr(message, "reasoning_content", None):
    print("reasoning:", message.reasoning_content)
print("answer:", message.content)

Reasoning effort

Reasoning models accept a reasoning_effort parameter that controls how much compute the model spends on internal reasoning before generating its response.

chat_completion = client.chat.completions.create(
    model="zai-org/GLM-5.3-Flash",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    reasoning_effort="high",
)

Supported values: none, minimal, low, medium, high, xhigh, max. Not all models support all values — unsupported combinations return a 400 error. When omitted, reasoning models use their default effort level. For vLLM/SGLang-hosted models, enable_thinking=false overrides reasoning_effort and disables reasoning entirely.

Chat Completions Compatibility

Parameter	Telnyx	OpenAI
`messages`	✅	✅
`model`	✅	✅
`stream`	✅	✅
`max_tokens`	✅	✅
`temperature`	✅	✅
`top_p`	✅	✅
`frequency_penalty`	✅	✅
`presence_penalty`	✅	✅
`n`	✅	✅
`stop`	✅	✅
`logit_bias`	✅	✅
`logprobs`	✅	✅
`top_logprobs`	✅	✅
`seed`	✅	✅
`response_format`	✅	✅
`tool_choice`	✅	✅
`tools`	✅	✅
`function`	✅	✅
`reasoning_effort`	✅	✅ (o-family/GPT-5 only)
`retrieval`	✅	❌
`guided_json`	✅	❌
`guided_regex`	✅	❌
`guided_choice`	✅	❌
`min_p`	✅	❌
`use_beam_search`	✅	❌
`best_of`	✅	❌
`length_penalty`	✅	❌
`early_stopping`	✅	❌
`user`	❌	✅

Transcriptions Compatibility

Parameter	Telnyx	OpenAI
`file`	✅	✅
`model`	✅	✅
`response_format`	✅	✅
`timestamp_granularities[]` → `segment`	✅	✅
`timestamp_granularities[]` → `word`	❌	✅
`language`	❌	✅
`prompt`	❌	✅
`temperature`	❌	✅