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Home/Models/OpenAI/GPT-5.1
O

GPT-5.1

Input:$1.25/M
Output:$10/M
GPT-5.1 is a general-purpose instruction-tuned language model focused on text generation and reasoning across product workflows. It supports multi-turn dialogue, structured output formatting, and code-oriented tasks such as drafting, refactoring, and explanation. Typical uses include chat assistants, retrieval-augmented QA, data transformation, and agent-style automation with tools or APIs when supported. Technical highlights include text-centric modality, instruction following, JSON-style outputs, and compatibility with function calling in common orchestration frameworks.
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Technical Specifications of kling-identify-face

SpecificationDetails
Model IDkling-identify-face
Model TypeImage analysis / face identification
Primary FunctionIdentify faces within submitted image inputs
Input FormatImage file or image URL, depending on endpoint configuration
Output FormatStructured identification results with face-related metadata
Integration MethodREST API
AuthenticationAPI key
Typical Use CasesFace lookup, identity matching workflows, media management, security review pipelines

What is kling-identify-face?

kling-identify-face is a face identification model available through CometAPI for detecting and identifying faces from image-based inputs. It is designed for workflows where applications need to submit an image, process facial information, and receive structured results that can be used in downstream systems or user-facing experiences.
This model can be useful in scenarios such as content organization, identity verification support flows, digital asset indexing, and automated media analysis. By exposing the capability through a standard API interface, CometAPI makes it easier for developers to integrate face identification into existing products without building the underlying model infrastructure themselves.

Main features of kling-identify-face

  • Face identification: Processes submitted images to identify faces and return machine-readable results for application use.
  • Structured API output: Returns results in a format that is easier to parse, validate, and integrate into automated systems.
  • Image-based workflow support: Fits applications that rely on uploaded images or image URLs as the primary input source.
  • Developer-friendly integration: Can be connected to backend services, internal tools, and production pipelines through standard API calls.
  • Scalable deployment option: Helps teams add face identification capabilities without managing model hosting or inference infrastructure directly.

How to access and integrate kling-identify-face

Step 1: Sign Up for API Key

Sign up on CometAPI and generate your API key from the dashboard. This key will be used to authenticate every request you send to the kling-identify-face API.

Step 2: Send Requests to kling-identify-face API

After getting your API key, send POST requests to the CometAPI endpoint for kling-identify-face. Include your API key in the Authorization header and provide the required input payload based on your use case.

curl --request POST \
  --url https://api.cometapi.com/v1/models/kling-identify-face \
  --header "Authorization: Bearer YOUR_COMETAPI_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "input": "YOUR_IMAGE_INPUT"
  }'

Step 3: Retrieve and Verify Results

Once the request is processed, the API will return the model output for kling-identify-face. Verify the response fields, handle any confidence or matching data returned by the model, and then pass the validated results into your application workflow or storage layer.

Features for GPT-5.1

Explore the key features of GPT-5.1, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.

Pricing for GPT-5.1

Explore competitive pricing for GPT-5.1, 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-5.1 can enhance your projects while keeping costs manageable.
Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Input:$1.25/M
Output:$10/M
Input:$1.5625/M
Output:$12.5/M
-20%

Sample code and API for GPT-5.1

GPT 5.1 API is what GPT-5.1 Thinking is the advanced reasoning variant of OpenAI’s GPT-5.1 family, it prioritizes adaptive, higher-quality reasoning while giving developers explicit control over the latency / compute trade-off.
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)
response = client.responses.create(
    model="gpt-5.1", input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

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)
response = client.responses.create(
    model="gpt-5.1", input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

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;
const base_url = "https://api.cometapi.com/v1";

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

const response = await openai.responses.create({
  model: "gpt-5.1",
  input: "Tell me a three sentence bedtime story about a unicorn.",
});

console.log(response);

Curl Code Example

curl https://api.cometapi.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "gpt-5.1",
    "input": "Tell me a three sentence bedtime story about a unicorn."
  }'

More Models

O

GPT-5.2 Chat

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GPT-5.2 Chat

Input:$1.75/M
Output:$14/M
gpt-5.2-chat-latest is the Chat-optimized snapshot of OpenAI’s GPT-5.2 family (branded in ChatGPT as GPT-5.2 Instant). It is the model for interactive/chat use cases that need a blend of speed, long-context handling, multimodal inputs and reliable conversational behaviour.
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GPT-5.2

Input:$1.75/M
Output:$14/M
GPT-5.2 is a multi-flavored model suite (Instant, Thinking, Pro) engineered for better long-context understanding, stronger coding and tool use, and materially higher performance on professional “knowledge-work” benchmarks.
O

GPT-5.1 Chat

O

GPT-5.1 Chat

Input:$1.25/M
Output:$10/M
GPT-5.1 Chat is an instruction-tuned conversational language model for general-purpose chat, reasoning, and writing. It supports multi-turn dialogue, summarization, drafting, knowledge-base QA, and lightweight code assistance for in-app assistants, support automation, and workflow copilots. Technical highlights include chat-optimized alignment, controllable and structured outputs, and integration paths for tool invocation and retrieval workflows when available.
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Gemini 2.5 Flash

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Gemini 2.5 Flash

Input:$0.3/M
Output:$7/M
Gemini 2.5 Flash is an AI model developed by Google, designed to provide fast and cost-effective solutions for developers, especially for applications requiring enhanced Inference capabilities. According to the Gemini 2.5 Flash preview announcement, the model was released in preview on April 17, 2025, supports Multimodal input, and has a context window of 1 million tokens. This model supports a maximum context length of 65,536 tokens.
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Gemini 2.5 Pro DeepSearch

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Input:$10/M
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Deep search model, with enhanced deep search and information retrieval capabilities, an ideal choice for complex knowledge integration and analysis.
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Gemini 2.5 Pro (All)

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Gemini 2.5 Pro (All)

Input:$1.25/M
Output:$2.5/M
Gemini 2.5 Pro (All) is a multimodal model for text and media understanding, designed for general-purpose assistants and grounded reasoning. It handles instruction following, analytical writing, code comprehension, and image/audio understanding with reliable tool/function calling and RAG-friendly behavior. Typical uses include enterprise chat agents, document and UI analysis, visual question answering, and workflow automation. Technical highlights include unified image‑text‑audio inputs, long-context support, structured JSON output, streaming responses, and system-instruction control.

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