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Home/Models/OpenAI/GPT-5.3 Chat
O

GPT-5.3 Chat

Input:$1.4/M
Output:$11.2/M
GPT-5.3 Instant model used in ChatGPT
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GPT-5.3 Chat (Alias: gpt-5.3-chat-latest) — Overview

GPT-5.3 Chat is the latest production chat model from OpenAI, offered as the gpt-5.3-chat-latest endpoint in the official API and powering ChatGPT’s day-to-day conversational experience. It focuses on improving everyday interaction quality—making responses smoother, more accurate, and better contextualized—while maintaining strong technical capabilities inherited from the broader GPT-5 family. :contentReference[oaicite:1]{index=1}

📊 Technical Specifications

SpecificationDetails
Model name/aliasGPT-5.3 Chat / gpt-5.3-chat-latest
ProviderOpenAI
Context window128,000 tokens
Max output tokens per request16,384 tokens
Knowledge cutoffAugust 31, 2025
Input modalitiesText and image inputs (vision only)
Output modalitiesText
Function callingSupported
Structured outputsSupported
Streaming responsesSupported
Fine-tuningNot supported
Distillation / embeddingsDistillation not supported; embeddings supported
Typical use endpointsChat completions, Responses, Assistants, Batch, Realtime
Function calling & toolsFunction calling enabled; supports web & file search via Responses API

🧠 What Makes GPT-5.3 Chat Unique

GPT-5.3 Chat represents an incremental refinement of Chat-oriented capabilities in the GPT-5 lineage. The core goal of this variant is to provide more natural, contextually coherent, and user-friendly conversational responses than earlier models like GPT-5.2 Instant. Improvements are oriented toward:

  • Dynamic, natural tone with fewer unhelpful disclaimers and more direct answers.
  • Better context understanding and relevance in common chat scenarios.
  • Smoother integration with rich chat use cases including multi-turn dialogue, summarization, and conversational assistance.

GPT-5.3 Chat is recommended for developers and interactive applications that need the latest conversational improvements without the specialized reasoning depth of future “Thinking” or “Pro” GPT-5.3 variants (which are forthcoming).

🚀 Key Features

  • Large Chat Context Window: 128K tokens enables rich conversation histories and long context tracking. :contentReference[oaicite:17]{index=17}
  • Improved Response Quality: Refined conversational flow with fewer unnecessary caveats or overly cautious refusals. :contentReference[oaicite:18]{index=18}
  • Official API Support: Fully supported endpoints for chat, batch processing, structured outputs, and real-time workflows.
  • Versatile Input Support: Accepts and contextualizes text and image inputs, suitable for multimodal chat use cases.
  • Function Calling & Structured Output: Enables structured and interactive application patterns via the API. :contentReference[oaicite:21]{index=21}
  • Broad Ecosystem Compatibility: Works with v1/chat/completions, v1/responses, Assistants, and other modern OpenAI API interfaces.

📈 Typical Benchmarks & Behavior

📈 Benchmark Performance

OpenAI and independent reports show improved real-world performance:

MetricGPT-5.3 Instant vs GPT-5.2 Instant
Hallucination rate with web search−26.8%
Hallucination rate without search−19.7%
User-flagged factual errors (web)~−22.5%
User-flagged factual errors (internal)~−9.6%

Notably, GPT-5.3’s focus on real-world conversational quality means benchmark score improvements (like standardized NLP metrics) are less of a release highlight — improvements show up most clearly in user experience metrics instead of raw test scores.

In industry comparisons, GPT-5-family chat variants are known to outperform earlier GPT-4 modules on everyday chat relevance and contextual tracking, though specialized reasoning tasks may still favor dedicated “Pro” variants or reasoning-optimized endpoints.

🤖 Use Cases

GPT-5.3 Chat is well-suited for:

  • Customer support bots and conversational assistants
  • Interactive tutorial or educational agents
  • Summarization and conversational search
  • Internal knowledge agents and team chat helpers
  • Multimodal Q&A (text + images)

Its balance of conversational quality and API versatility makes it ideal for interactive applications that combine natural dialogue with structured data outputs.

🔍 Limitations

  • Not the deepest reasoning variant: For mission-critical, high-stakes analytical depth, forthcoming GPT-5.3 Thinking or Pro models may be more appropriate.
  • Multimodal outputs limited: While input images are supported, full image/video generation or rich multimodal output workflows are not the primary focus of this variant.
  • Fine-tuning is not supported: You cannot fine-tune this model, though you can steer behavior via system prompts.

How to access GPT-5.3 Chat API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

cometapi-key

Step 2: Send Requests to GPT-5.3 Chat API

Select the “gpt-5.3-chat-latest” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is Chat Completions

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

FAQ

What is the context window of GPT-5.3 Chat API (gpt-5.3-chat-latest)?

GPT-5.3 Chat supports a 128,000 token context window, allowing long multi-turn conversations and large document inputs within a single request.

What is the maximum output token limit for GPT-5.3 Chat API?

The model can generate up to 16,384 output tokens per request, depending on the max_tokens parameter you set.

Does GPT-5.3 Chat API support image inputs?

Yes, GPT-5.3 Chat accepts both text and image inputs, enabling multimodal chat scenarios such as visual question answering.

Does GPT-5.3 Chat API support function calling and structured outputs?

Yes, GPT-5.3 Chat supports function calling, JSON schema structured outputs, streaming, and tool integrations through the Responses API.

Can I fine-tune GPT-5.3 Chat API for custom behavior?

No, GPT-5.3 Chat does not support fine-tuning. Behavior customization must be achieved through system prompts and tool orchestration.

What is the knowledge cutoff for GPT-5.3 Chat?

GPT-5.3 Chat has a knowledge cutoff of August 31, 2025, meaning it was trained on data available up to that date.

When should I use GPT-5.3 Chat API instead of GPT-5.2 Instant?

Choose GPT-5.3 Chat for improved conversational fluency, better context tracking, and more refined response quality, while GPT-5.2 Instant may be suitable for lighter-weight or earlier-generation chat use cases.

Features for GPT-5.3 Chat

Explore the key features of GPT-5.3 Chat, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.

Pricing for GPT-5.3 Chat

Explore competitive pricing for GPT-5.3 Chat, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how GPT-5.3 Chat can enhance your projects while keeping costs manageable.
Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Input:$1.4/M
Output:$11.2/M
Input:$1.75/M
Output:$14/M
-20%

Sample code and API for GPT-5.3 Chat

Access comprehensive sample code and API resources for GPT-5.3 Chat to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of GPT-5.3 Chat in your projects.
POST
/v1/chat/completions
Python
JavaScript
Curl
from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

completion = client.chat.completions.create(
    model="gpt-5.3-chat-latest",
    messages=[
        {"role": "developer", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

print(completion.choices[0].message)

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

completion = client.chat.completions.create(
    model="gpt-5.3-chat-latest",
    messages=[
        {"role": "developer", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

print(completion.choices[0].message)

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const api_key = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const base_url = "https://api.cometapi.com/v1";

const openai = new OpenAI({
  apiKey: api_key,
  baseURL: base_url,
});

const completion = await openai.chat.completions.create({
  messages: [
    { role: "developer", content: "You are a helpful assistant." },
    { role: "user", content: "Hello!" },
  ],
  model: "gpt-5.3-chat-latest",
});

console.log(completion.choices[0].message);

Curl Code Example

# Get your CometAPI key from https://api.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "gpt-5.3-chat-latest",
    "messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

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