Reasoning

Some Model APIs support extended thinking, where the model reasons through a problem before producing a final answer. The reasoning process generates additional tokens that appear in a separate reasoning_content field, distinct from the final response.

Supported models

Model	Slug	Reasoning
DeepSeek V4 Pro	`deepseek-ai/DeepSeek-V4-Pro`	Enabled by default
Inkling	`thinkingmachines/inkling`	Enabled by default
OpenAI GPT 120B	`openai/gpt-oss-120b`	Enabled by default
Kimi K2.5	`moonshotai/Kimi-K2.5`	Opt-in through `chat_template_args`
Kimi K2.6	`moonshotai/Kimi-K2.6`	Opt-in through `chat_template_args`
Kimi K2.7 Code	`moonshotai/Kimi-K2.7-Code`	Opt-in through `chat_template_args`
GLM 4.7	`zai-org/GLM-4.7`	Opt-in through `chat_template_args`
GLM 5	`zai-org/GLM-5`	Opt-in through `chat_template_args`
GLM 5.1	`zai-org/GLM-5.1`	Opt-in through `chat_template_args`
GLM 5.2	`zai-org/GLM-5.2`	Opt-in through `chat_template_args`
Nemotron Super	`nvidia/Nemotron-120B-A12B`	Opt-in through `chat_template_args`
Nemotron Ultra	`nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B`	Opt-in through `chat_template_args`

DeepSeek V4 Pro, Inkling, OpenAI GPT 120B, and GLM 5.2 also support reasoning_effort. For Inkling, reasoning_effort is the only reasoning control: chat_template_args: {"enable_thinking": false} doesn’t disable its thinking; reasoning_effort: "none" does. Models not listed here don’t support reasoning.

Enable thinking

For models marked opt-in in the table above, enable thinking by passing chat_template_args.

Python
JavaScript
cURL

Pass chat_template_args through extra_body since it extends the standard OpenAI API:

enable_thinking.py

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    extra_body={"chat_template_args": {"enable_thinking": True}},
    max_tokens=4096,
    stream=True,
)

Include chat_template_args directly in the request options:

enable_thinking.js

const response = await client.chat.completions.create({
    model: "moonshotai/Kimi-K2.5",
    messages: [{ role: "user", content: "What is the sum of the first 100 prime numbers?" }],
    chat_template_args: { enable_thinking: true },
    max_tokens: 4096,
    stream: true,
});

Include chat_template_args in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "moonshotai/Kimi-K2.5",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "chat_template_args": {"enable_thinking": true},
    "max_tokens": 4096,
    "stream": false
  }'

Control reasoning depth

The reasoning_effort parameter controls how thoroughly the model reasons through a problem. DeepSeek V4 Pro, Inkling, OpenAI GPT 120B, and GLM 5.2 support this parameter. Supported values vary by model:

Model	Supported values
DeepSeek V4 Pro	`none`, `minimal`, `low`, `medium` (default), `high`, `xhigh`, `max`
Inkling	`none`, `minimal`, `low`, `medium`, `high`, `xhigh`
OpenAI GPT 120B	`none`, `minimal`, `low`, `medium` (default), `high`, `xhigh`, `max`
GLM 5.2	`none`, `high`, `max`

Lower values return faster responses with less thorough reasoning; higher values reason longer and cost more output tokens. none disables reasoning entirely. GLM 5.2 returns a 400 error for values outside its set. Some model templates also read reasoning_effort from inside chat_template_args (GLM 5.2 honors both placements). Use the top-level parameter: the API validates it and returns a 400 for invalid values, but doesn’t validate chat_template_args contents, so mistakes there fail silently.

A successful request doesn’t mean reasoning_effort took effect. Models not listed in this table accept the parameter but ignore it.

DeepSeek V4 Pro
OpenAI GPT 120B
GLM 5.2

Python
JavaScript
cURL

Pass reasoning_effort through extra_body since it extends the standard OpenAI API:

reasoning_effort.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ.get("BASETEN_API_KEY")
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",
    messages=[
        {"role": "user", "content": "What is the sum of the first 100 prime numbers?"}
    ],
    extra_body={"reasoning_effort": "high"}  
)

print(response.choices[0].message.content)

Include reasoning_effort directly in the request options:

reasoning_effort.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "deepseek-ai/DeepSeek-V4-Pro",
    messages: [
        { role: "user", content: "What is the sum of the first 100 prime numbers?" }
    ],
    reasoning_effort: "high"
});

console.log(response.choices[0].message.content);

Include reasoning_effort in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Pro",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "reasoning_effort": "high"
  }'

Python
JavaScript
cURL

Pass reasoning_effort through extra_body since it extends the standard OpenAI API:

reasoning_effort.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ.get("BASETEN_API_KEY")
)

response = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    messages=[
        {"role": "user", "content": "What is the sum of the first 100 prime numbers?"}
    ],
    extra_body={"reasoning_effort": "high"}  
)

print(response.choices[0].message.content)

Include reasoning_effort directly in the request options:

reasoning_effort.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "openai/gpt-oss-120b",
    messages: [
        { role: "user", content: "What is the sum of the first 100 prime numbers?" }
    ],
    reasoning_effort: "high"
});

console.log(response.choices[0].message.content);

Include reasoning_effort in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "openai/gpt-oss-120b",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "reasoning_effort": "high"
  }'

Python
JavaScript
cURL

Pass reasoning_effort through extra_body since it extends the standard OpenAI API:

reasoning_effort.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ.get("BASETEN_API_KEY")
)

response = client.chat.completions.create(
    model="zai-org/GLM-5.2",
    messages=[
        {"role": "user", "content": "What is the sum of the first 100 prime numbers?"}
    ],
    extra_body={"reasoning_effort": "high"}  
)

print(response.choices[0].message.content)

Include reasoning_effort directly in the request options:

reasoning_effort.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "zai-org/GLM-5.2",
    messages: [
        { role: "user", content: "What is the sum of the first 100 prime numbers?" }
    ],
    reasoning_effort: "high"
});

console.log(response.choices[0].message.content);

Include reasoning_effort in the JSON request body:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "zai-org/GLM-5.2",
    "messages": [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}],
    "reasoning_effort": "high"
  }'

Reasoning improves quality for tasks that benefit from step-by-step thinking: mathematical calculations, multi-step logic problems, code generation with complex requirements, and analysis requiring multiple considerations. For straightforward tasks like simple Q&A or text generation, reasoning adds latency and token cost without improving quality. In these cases, use a model without reasoning support or set reasoning_effort to low.

Parse the response

The model’s thinking process appears in reasoning_content, separate from the final answer in content. Both fields are returned on the message object.

Python
JavaScript
cURL

Read reasoning_content and content directly off the message object:

parse_reasoning.py

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.baseten.co/v1",
    api_key=os.environ.get("BASETEN_API_KEY"),
)

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.6",
    messages=[{"role": "user", "content": "Is 91 a prime number? Answer in one sentence."}],
    extra_body={"chat_template_args": {"enable_thinking": True}},
)

message = response.choices[0].message
print("Reasoning:", message.reasoning_content)
print("Answer:", message.content)

Read reasoning_content and content from the returned message:

parse_reasoning.js

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://inference.baseten.co/v1",
    apiKey: process.env.BASETEN_API_KEY,
});

const response = await client.chat.completions.create({
    model: "moonshotai/Kimi-K2.6",
    messages: [{ role: "user", content: "Is 91 a prime number? Answer in one sentence." }],
    chat_template_args: { enable_thinking: true },
});

const message = response.choices[0].message;
console.log("Reasoning:", message.reasoning_content);
console.log("Answer:", message.content);

Pipe the response through jq to extract each field:

Request

curl https://inference.baseten.co/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "moonshotai/Kimi-K2.6",
    "messages": [{"role": "user", "content": "Is 91 a prime number? Answer in one sentence."}],
    "chat_template_args": {"enable_thinking": true}
  }' | jq '.choices[0].message | {reasoning: .reasoning_content, answer: .content}'

The response body contains both fields on the assistant message:

Response

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "reasoning_content": "The user is asking whether 91 is a prime number... 91 = 7 × 13, so it is not prime...",
        "content": "No, 91 is not a prime number because it can be factored as $7 \\times 13$."
      }
    }
  ],
  "usage": {
    "prompt_tokens": 21,
    "completion_tokens": 203,
    "total_tokens": 224
  }
}

Reasoning tokens are included in completion_tokens and count toward your total usage and billing.

Overview

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Training

Organization

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Troubleshooting

Supported models

Enable thinking

Control reasoning depth

Parse the response

Next steps

Model APIs overview

Structured outputs

​Supported models

​Enable thinking

​Control reasoning depth

​Parse the response

​Next steps

Model APIs overview

Structured outputs

Supported models

Enable thinking

Control reasoning depth

Parse the response

Next steps