Model-specific Notes | Parasail

For the complete documentation index, see llms.txt. This page is also available as Markdown.

Most Serverless models work with the standard Chat Completions API and sampling parameters. Some models expose extra controls through extra_body, custom_params, or chat_template_kwargs.

DeepSeek V3.1 thinking mode

Use chat_template_kwargs.thinking to turn thinking mode on or off. Pass a JSON boolean, not a string.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["PARASAIL_API_KEY"],
)

chat_completion = client.chat.completions.create(
    model="parasail-deepseek-31",
    messages=[{"role": "user", "content": "What is the capital of New York?"}],
    extra_body={"chat_template_kwargs": {"thinking": True}},
)

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

curl -X POST https://api.parasail.io/v1/chat/completions \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "parasail-deepseek-31",
    "chat_template_kwargs": {"thinking": false},
    "messages": [{"role": "user", "content": "What is the capital of New York?"}]
  }'

Qwen3.5 thinking mode

Use chat_template_kwargs.enable_thinking to turn Qwen3.5 thinking mode on or off. Qwen-specific sampling settings such as top_k belong in extra_body.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["PARASAIL_API_KEY"],
)

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-35B-A3B",
    messages=[{"role": "user", "content": "What is the capital of New York?"}],
    max_tokens=32768,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    },
)

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

GPT-OSS reasoning control

GPT-OSS models support explicit reasoning controls that trade depth for latency and cost.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["PARASAIL_API_KEY"],
)

resp = client.chat.completions.create(
    model="parasail-gpt-oss-20b-fast",
    messages=[{"role": "user", "content": "Explain why the sky is blue."}],
    extra_body={
        "custom_params": {"thinking_budget": 40},
        "chat_template_kwargs": {"reasoning_effort": "high"},
    },
)

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

curl https://api.parasail.io/v1/chat/completions \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "parasail-gpt-oss-20b-fast",
    "messages": [{"role": "user", "content": "Explain why the sky is blue."}],
    "custom_params": {"thinking_budget": 40},
    "chat_template_kwargs": {"reasoning_effort": "high"}
  }'

Practical defaults

Structured outputs with GPT-OSS

Use response_format.type = "json_schema" with GPT-OSS models when the response must be valid JSON that matches a schema. You can combine structured output with GPT-OSS reasoning controls in the same request: put response_format at the top level, and put thinking_budget and reasoning_effort in extra_body.

import json
import os

from openai import OpenAI

MODEL = "parasail-gpt-oss-20b-fast"

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["PARASAIL_API_KEY"],
)

product_review_schema = {
    "type": "object",
    "properties": {
        "rating": {"type": "integer", "minimum": 1, "maximum": 5},
        "pros": {"type": "array", "items": {"type": "string"}},
        "cons": {"type": "array", "items": {"type": "string"}},
        "summary": {"type": "string"},
    },
    "required": ["rating", "pros", "cons", "summary"],
    "additionalProperties": False,
}

resp = client.chat.completions.create(
    model=MODEL,
    messages=[
        { "role": "user", "content": "Write a short review of a flagship smartphone."},
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "product_review",
            "strict": True,
            "schema": product_review_schema,
        },
    },
    extra_body={
        "custom_params": {"thinking_budget": 50},
        "chat_template_kwargs": {"reasoning_effort": "medium"},
    },
)

data = json.loads(resp.choices[0].message.content)
print(data)

Parsing and strictness notes:

• Use strict: True with additionalProperties: False to keep the response limited to the schema.

• Parse resp.choices[0].message.content with json.loads() and use the parsed object as the source of truth.

• For streaming responses, concatenate the JSON text from delta.content chunks and parse it after the stream ends.

For full schema guidance, see Structured Output.

Next steps

• Serverless overview
• Chat Completions API
• Parameters reference
• Structured Output