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GPT-5.2 Chat

GPT-5.2 Chat is an OpenAI conversational language model designed for interactive dialogue and task assistance.

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What is GPT-5.2 Chat?

GPT-5.2 Chat is a conversational AI model from OpenAI optimized for multi-turn dialogue and natural language understanding. It is mainly used for chat-based assistance, such as answering questions, drafting and editing text, and helping users reason through complex problems. It also supports integration into applications and workflows where reliable, instruction-following dialogue is required. GPT-5.2 Chat belongs to OpenAI’s GPT family of large language models, following earlier generations such as GPT-3 and GPT-4.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
Azure OpenAI $1.75 $14.00 $0.18 ~170ms ~70 tps 100.00%
OpenAI $1.75 $14.00 $0.18 ~150ms ~80 tps 99.06%

Try this model

Test GPT-5.2 Chat right here — free to start.

GPT-5.2 Chat
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="openai/gpt-5-2-chat",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "openai/gpt-5-2-chat",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

Uptime

30-Day Uptime
99.06%
Past Incidents (30d)
2
Error rate (24h)
0.00%

Last 30 days

28/30 days operational | 99.06% uptime

30 days ago Today
Operational Degraded Outage Maintenance
See All Incidents

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn conversations, maintaining context, following instructions, and adapting tone for assistance, brainstorming, and problem-solving.

  • Code Reasoning

    Understands, writes, and explains code across multiple languages, assisting with debugging, refactoring, and algorithmic reasoning tasks.

  • Image Understanding

    Interprets images to identify objects, text, layouts, and visual relationships, supporting analysis, explanation, and content extraction.

  • Text Translation

    Translates between many languages, preserving meaning and style, and can clarify ambiguities or cultural nuances when needed.

  • Visual Text OCR

    Extracts readable text from images, including documents, screenshots, and signs, enabling search, editing, and downstream processing.

6 Most Valuable Use Cases

  • Customer Support Chatbot
  • Financial Report Summaries
  • Legal Document Review
  • Regulatory Change Monitoring
  • Marketing Content Generation
  • Code Review Assistance

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Dynamic AI Routing

    Automatically route each request to the optimal model across providers based on latency, reliability, and capabilities, so you ship faster without hardcoding vendor logic.

    One endpoint, any model

  • Cost-Aware Orchestration

    Balance quality and price with per-call cost controls, smart tiering, and spend visibility, letting you optimize inference budgets without rewriting application code.

    Maximum performance per dollar

  • Resilient Fallback Flows

    Define automatic failover to backup models or providers on errors, timeouts, and rate limits, keeping your AI features online even when vendors aren’t.

    Built-in reliability layer

  • End-to-End Observability

    Get centralized logs, traces, and metrics for every model call—latency, errors, and tokens—so you can debug issues and tune performance from a single dashboard.

    See every token, everywhere

  • Task-Level Abstractions

    Describe tasks—chat, generation, tools, scoring—once and let LLM.API handle provider-specific quirks, so you focus on product logic instead of API plumbing.

    Code to tasks, not vendors

  • High-Throughput Batch Jobs

    Run large-scale batch inference across models and providers with concurrency, retries, and progress tracking handled for you, ideal for backfills and async workloads.

    Ship millions of calls safely

When to Use — When NOT to Use

Use it if...

  • You need strong general-purpose chat, coding, and analysis with minimal integration overhead.
  • You need advanced reasoning over complex instructions, including planning, refactoring, and debugging code.
  • You need high-quality natural language generation for support bots, agents, or content drafting.
  • You need tight integration with other OpenAI models, tools, or function-calling ecosystems.
  • Your use case involves multi-step workflows where the assistant must maintain long, coherent context.
  • You need good performance on ambiguous user queries that require clarification and safe handling.
  • Your use case involves mixing natural language, structured data, and light mathematical reasoning.

Avoid if...

  • You need strict on-premise deployment with no external API calls or cloud dependency.
  • Your workload requires ultra-low latency, sub-50ms responses for tight real-time interactivity.
  • You need deterministic, auditable outputs where non-probabilistic rule-based systems are mandatory.
  • Your workload requires heavy, high-frequency numerical computation better suited to traditional GPU code.
  • You need guaranteed compliance with highly specialized domain regulations beyond configurable policies.
  • Your workload requires fully offline operation, disconnected from the internet or external services.
  • You need a tiny, device-embedded model optimized for inference on low-power hardware.

Frequently Asked Questions

  • What is GPT-5.2 Chat?

    GPT-5.2 Chat is a state-of-the-art OpenAI conversational language model accessible through the LLM.API unified gateway for general-purpose and agentic applications.

  • What is GPT-5.2 Chat best suited for?

    GPT-5.2 Chat excels at complex multi-step reasoning, tool-using agents, high-quality coding assistance, and production chatbots requiring reliable, steerable behavior.

  • What modalities does GPT-5.2 Chat support via LLM.API?

    GPT-5.2 Chat supports text input and output via LLM.API; additional modalities depend on LLM.API’s configured OpenAI feature support.

  • How is GPT-5.2 Chat priced on LLM.API?

    GPT-5.2 Chat pricing is defined by LLM.API’s OpenAI-backed tariff; refer to your LLM.API dashboard or pricing docs for current per-token rates.

  • What context window does GPT-5.2 Chat support?

    GPT-5.2 Chat supports a large context window; check the LLM.API model metadata for the exact maximum tokens for your deployment.

  • How fast is GPT-5.2 Chat in terms of latency?

    Typical end-to-end latency depends on prompt size and LLM.API infrastructure, but GPT-5.2 Chat is optimized for responsive interactive use.

  • How do I call GPT-5.2 Chat through the LLM.API?

    Specify the model identifier "GPT-5.2 Chat" in your LLM.API completion or chat endpoint request, plus your prompt and any desired parameters.

  • How does GPT-5.2 Chat compare to earlier GPT-4.x models?

    GPT-5.2 Chat generally provides stronger reasoning, better adherence to instructions, and improved coding capabilities compared with GPT-4.x-class models.

  • What limitations does GPT-5.2 Chat have?

    GPT-5.2 Chat can still hallucinate, reflect outdated knowledge, and must not be solely relied on for high-stakes domains without external verification.

  • Can GPT-5.2 Chat use tools or functions through LLM.API?

    Yes, if LLM.API exposes a tool-calling interface, GPT-5.2 Chat can be configured to call tools or functions based on your schema.

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