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Some models in Tera’s catalog produce explicit reasoning traces — the chain-of-thought the model used before arriving at its answer. Tera surfaces these as a separate reasoning_content field so they don’t pollute the visible response. Thinking models in the catalog today:

​
Response shape

Non-streaming responses get a reasoning_content sibling of content: 
{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "reasoning_content": "The user is asking about... I should consider...",
        "content": "The answer is 42."
      },
      "finish_reason": "stop"
    }
  ]
}
reasoning_content is the model’s internal trace; content is the user-facing answer.

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Streaming

When stream: true, reasoning deltas arrive first, then content deltas: 
data: {"choices":[{"delta":{"role":"assistant","reasoning_content":"The user is"}}]}
data: {"choices":[{"delta":{"reasoning_content":" asking about"}}]}
...
data: {"choices":[{"delta":{"content":"The answer is"}}]}
data: {"choices":[{"delta":{"content":" 42."}}]}
data: {"choices":[{"delta":{},"finish_reason":"stop"}]}
data: [DONE]

from openai import OpenAI

client = OpenAI(base_url="https://api.tera.gw/v1", api_key="sk-tera-...")

stream = client.chat.completions.create(
    model="Qwen/Qwen3.5-27B",
    messages=[{"role": "user", "content": "What's 6 times 7?"}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if reasoning := getattr(delta, "reasoning_content", None):
        print(f"\033[2m{reasoning}\033[0m", end="", flush=True)
    if content := delta.content:
        print(content, end="", flush=True)

​
Should you show reasoning to end users?

Up to you. Common patterns:
  • Hide entirely — drop reasoning_content, display only content.
  • Show collapsible — UI affordance like “Show reasoning” that reveals the trace.
  • Use for logging only — keep traces server-side for debugging and feedback loops.
Reasoning traces consume output tokens and contribute to your bill. Use max_tokens to bound total generation length.

​
Why a separate field?

OpenAI clients expect content to be the user-facing answer. Mixing reasoning markers (the literal think tags emitted by the model) into content breaks downstream parsers. By separating the two, OpenAI SDKs work without modification and reasoning becomes an opt-in feature on the client side.