Capabilities

Thinking

Both models reason before they answer. Choose how hard they think, and read the reasoning alongside the answer.

Reasoning effort

Set reasoning_effort to trade speed and cost for depth. none turns reasoning off.

Model	Accepted values
`minimax-m3.1-flash-abliterated`	`none` `low` `high` `max`
`deepseek-v4.1-flash-1017-abliterated`	`none` `low` `high` `xhigh` `max`

Any other value, medium included, gets 400 unsupported_parameter. The accepted values for each model are also listed in lyber.reasoning_effort_options from GET /v1/models.

main.py

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
    api_key=os.environ["LYBER_API_KEY"],
)

response = client.chat.completions.create(
    model="minimax-m3.1-flash-abliterated",
    messages=[{"role": "user", "content": "Is 1,000,003 prime?"}],
    reasoning_effort="high",
    max_tokens=8192,
)
message = response.choices[0].message
print(getattr(message, "reasoning_content", None))  # the model's reasoning
print(message.content)                              # the answer

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
  apiKey: process.env.LYBER_API_KEY,
});

const response = await client.chat.completions.create({
  model: "minimax-m3.1-flash-abliterated",
  messages: [{ role: "user", content: "Is 1,000,003 prime?" }],
  reasoning_effort: "high",
  max_tokens: 8192,
});
const message = response.choices[0].message as OpenAI.ChatCompletionMessage & { reasoning_content?: string };
console.log(message.reasoning_content); // the model's reasoning
console.log(message.content);           // the answer

curl https://lyber-paid-acceptance-gateway-production.up.railway.app/v1/chat/completions \
  -H "Authorization: Bearer $LYBER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3.1-flash-abliterated",
    "messages": [{"role": "user", "content": "Is 1,000,003 prime?"}],
    "reasoning_effort": "high",
    "max_tokens": 8192
  }'

Reading the reasoning

The reasoning comes back as plain text in message.reasoning_content, or delta.reasoning_content when streaming, separate from the answer in content. Some runtimes name the field reasoning; read either. Lyber passes the model's text through unchanged.

In multi-turn conversations you can send an assistant turn's reasoning_content back with it, or leave it out.

Thinking budget

The DeepSeek model also accepts thinking_token_budget, a cap on reasoning tokens. It must be no larger than max_tokens or 262,144, and can't be combined with reasoning_effort: "none".

body.json

{
  "model": "deepseek-v4.1-flash-1017-abliterated",
  "messages": [{"role": "user", "content": "Plan a three-step migration."}],
  "reasoning_effort": "high",
  "thinking_token_budget": 4096,
  "max_tokens": 8192
}

Billing

Reasoning tokens are output tokens: they count toward max_tokens and bill at the output rate. When the runtime reports it, usage.completion_tokens_details.reasoning_tokens shows how many there were. If the reasoning uses the whole allowance, the answer stops with finish_reason: "length"; raise max_tokens or lower the effort.