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Home/Models/OpenAI/GPT-5.1 Chat
O

GPT-5.1 Chat

入力:$1.25/M
出力:$10/M
文脈:400.0k
最大出力:128.0k
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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gpt-5.1-chat-latest API, is OpenAI’s GPT-5.1 Instant that is the low-latency variant of the newly released GPT-5.1 family (announced November 12, 2025). It’s designed to deliver the “most-used” ChatGPT experience with faster turn-taking, warmer conversational tone defaults, improved instruction following, and a built-in adaptive-reasoning capability that decides when to reply immediately and when to spend extra compute to “think” through harder queries.

Basic information & features

  • Warmer, more conversational default tone and expanded tone/personalization presets to match user preferences (examples: Professional, Friendly, Candid, Quirky, Efficient, Nerdy, Cynical).
  • Adaptive reasoning: the model decides when to take extra reasoning steps before answering; Instant aims to be fast on most everyday prompts while still using extra effort when appropriate.
  • Improved instruction-following (fewer misunderstandings on multi-step prompts) and generally reduced jargon for better user comprehension (especially in the Thinking variant).
  • Designed for real-time UX: streaming responses, low token-roundtrip latency useful for voice assistants, live transcription, and highly interactive conversational apps.

Technical details (developer-facing)

  • API model identifiers: OpenAI will expose Instant in the API under the chat-style identifier gpt-5.1-chat-latest (Instant) and gpt-5.1 for Thinking (per OpenAI’s release notes). Use the Responses API endpoint for best efficiency.
  • Response API & parameters: The GPT-5 family (including 5.1) is best used via the newer Responses API. Typical options you’ll pass include model name, input/messages, and optional control parameters like verbosity / reasoning (effort) that tune how much internal reasoning the model attempts before responding (assuming the platform follows the same parameter conventions introduced with GPT-5). For highly interactive apps, enable streaming replies.
  • Adaptive reasoning behaviour: Instant is tuned to favor quick replies but has light adaptive reasoning—it will allocate slightly more compute on tougher prompts (math, coding, multi-step reasoning) to reduce errors while keeping average latency low. GPT-5.1 Thinking will spend more compute on harder problems and less on trivial ones.

Benchmark & safety performance

GPT-5.1 Instant is tuned to keep responses fast while improving math and coding evals (AIME 2025, Codeforces improvements were specifically noted by OpenAI).

OpenAI published a GPT-5.1 System Card addendum with production benchmark metrics and targeted safety evaluations. Key figures (Production Benchmarks, higher = better, not_unsafe metric):

  • Illicit / non-violent (not_unsafe) — gpt-5.1-instant: 0.853.
  • Personal data — gpt-5.1-instant: 1.000 (perfect on this benchmark).
  • Harassment — gpt-5.1-instant: 0.836.
  • Mental health (new eval) — gpt-5.1-instant: 0.883.
  • StrongReject (jailbreak robustness, not_unsafe) — gpt-5.1-instant: 0.976 (shows strong robustness to adversarial jailbreaks compared with older instant checkpoints).

Typical and recommended use cases for GPT-5.1 Instant

  1. Chatbots & conversational UIs — customer support chat, sales assistants, and product guides where low latency preserves conversation flow.
  2. Voice assistants / streaming replies — streaming partial outputs to a UI or TTS engine for sub-second interactions.
  3. Summarization, rephrasing, message drafting — quick transformations that benefit from a warmer, user-friendly tone.
  4. Light coding help and inline debugging — for quick code snippets and suggestions; use Thinking for deeper bug hunts. (Test on your codebase.)
  5. Agent front-ends and retrieval-augmented workflows — where you want fast responses combined with occasional deeper reasoning/tool calls. Use the adaptive-reasoning behavior to balance cost vs. depth.

Comparison with other models

  • GPT-5.1 vs GPT-5: GPT-5.1 is a tuned upgrade — warmer default tone, improved instruction following, and adaptive reasoning. OpenAI positions 5.1 as strictly better in the areas they targeted, but retains GPT-5 in a legacy menu for transition/compatibility.
  • GPT-5.1 vs GPT-4.1 / GPT-4.5 / GPT-4o: GPT-5 family still targets higher reasoning and coding performance than GPT-4.x series; GPT-4.1 remains relevant for very long contexts or cost-sensitive deployments. Reporters emphasize GPT-5/5.1 lead on hard math/coding benchmarks, but exact per-task advantages depend on the benchmark.
  • GPT-5.1 vs Claude / Gemini / other rivals: early commentary frames GPT-5.1 as a response to user feedback (personality + capability). Competitors (Anthropic’s Claude Sonnet series, Google’s Gemini 3 Pro, Baidu’s ERNIE variants) emphasize different tradeoffs (safety-first, multimodality, massive contexts). For technical customers, evaluate across cost, latency, safety behavior on your workloads (prompts + tool calls + domain data).

GPT-5.1 Chatの機能

GPT-5.1 Chatのパフォーマンスと使いやすさを向上させるために設計された主要機能をご紹介します。これらの機能がプロジェクトにどのようなメリットをもたらし、ユーザーエクスペリエンスを改善するかをご確認ください。

GPT-5.1 Chatの料金

GPT-5.1 Chatの競争力のある価格設定をご確認ください。さまざまな予算や利用ニーズに対応できるよう設計されています。柔軟なプランにより、使用した分だけお支払いいただけるため、要件の拡大に合わせて簡単にスケールアップできます。GPT-5.1 Chatがコストを管理しながら、お客様のプロジェクトをどのように強化できるかをご覧ください。
コメット価格 (USD / M Tokens)
入力:$1.25/M
出力:$10/M

GPT-5.1 ChatのサンプルコードとAPI

OpenAI’s GPT-5.1 Instant is the low-latency variant of the newly released GPT-5.1 family (announced November 12, 2025). It’s designed to deliver the “most-used” ChatGPT experience with faster turn-taking, warmer conversational tone defaults, improved instruction following, and a built-in adaptive-reasoning capability that decides when to reply immediately and when to spend extra compute to “think” through harder queries.
POST
/v1/chat/completions
POST
/v1/responses
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-chat-latest", 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-chat-latest", 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-chat-latest",
  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-chat-latest",
    "input": "Tell me a three sentence bedtime story about a unicorn."
  }'

その他のモデル

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GPT-5.2 Chat

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GPT-5.2 Chat

入力:$1.75/M
出力:$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.
O

GPT-5.2

入力:$1.75/M
出力:$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.
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GPT-5.1

O

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入力:$1.25/M
出力:$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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出力:$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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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) 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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