GPT-5.2 is a large multimodal OpenAI model accessible via LLM.API, designed for advanced reasoning, coding, and content generation across text and image inputs.

What modalities does GPT-5.2 support through LLM.API?

GPT-5.2 supports text input and output, and can optionally process image inputs when invoked with the appropriate LLM.API parameters.

How is GPT-5.2 priced when used via LLM.API?

LLM.API meters GPT-5.2 usage based on tokens processed, with per-input and per-output token rates defined in LLM.API’s pricing documentation.

What is the context window of GPT-5.2?

GPT-5.2 supports a large-context window suitable for long conversations and multi-file prompts; check LLM.API docs for the exact current token limit.

How fast is GPT-5.2 in terms of latency and throughput?

GPT-5.2 is optimized for low latency and streaming responses, but actual speed depends on prompt size and concurrent load on LLM.API.

How do I call GPT-5.2 via LLM.API?

You select the GPT-5.2 model name in your LLM.API request payload, include your LLM.API key, then send standard chat or completion-style requests.

What is GPT-5.2 particularly good at?

GPT-5.2 excels at complex multi-step reasoning, code generation and refactoring, long-form writing, and following detailed instructions across domains.

How does GPT-5.2 compare to earlier OpenAI models like GPT-4.1?

GPT-5.2 generally offers stronger reasoning, better instruction following, and more robust handling of long context than GPT-4.1 at comparable usage patterns.

What limitations should I be aware of when using GPT-5.2?

GPT-5.2 can still hallucinate facts, misinterpret ambiguous instructions, and should not be used as the sole source for high-stakes decisions.

Can I fine-tune GPT-5.2 through LLM.API?

Fine-tuning support for GPT-5.2 depends on LLM.API’s current feature set; check their documentation for whether fine-tuning is enabled for this model.

Which endpoint do I call for GPT-5.2?

POST https://api.llmapi.ai/v1/chat/completions with model "gpt-5.2" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to GPT-5.2 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does GPT-5.2 support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format. Reasoning effort accepts none, low, medium, high, xhigh (default none).

GPT-5.2 API: Endpoint & 30% Discount

Up to 30%

GPT-5.2 is an OpenAI large language model in the GPT-5 family, designed for advanced natural language understanding and generation across many tasks.

Start Using API

What is GPT-5.2?

GPT-5.2 is a generative pre-trained transformer model from OpenAI for interpreting instructions and producing human-like text. It is mainly used for tasks such as drafting and editing content, answering questions, and assisting with coding or data analysis workflows. It is also applied in building conversational agents, research assistants, and domain-specific tools that require reliable language understanding and reasoning. GPT-5.2 follows earlier models in OpenAI’s GPT series, extending the capabilities introduced by GPT-4-class and GPT-5-class systems.

Can you self-host GPT-5.2?

No. We found no public weights for GPT-5.2 on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

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).

List price by provider

InputOutput

azure$1.75 in

$14 out

openai$1.75 in

$14 out

Provider list prices; the LLM.API discount applies on top.

Provider	Input	Output	Cache read /M	Latency	Throughput	Uptime
azure30% off	in $1.75; out $14 per 1M tokens	400K tokens	vision, tools, streaming, reasoning, web search, JSON, structured
openai	in $1.75; out $14 per 1M tokens	400K tokens	vision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test GPT-5.2 right here — free to start.

GPT-5.2

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gpt-5.2 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.2",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "none"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="none",
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gpt-5.2",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="gpt-5.2",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "gpt-5.2",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "none"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gpt-5.2 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Supported	`image_url content parts`
Web search	Supported	`web_search`
Reasoning effort	none · low · medium · high · xhigh	`reasoning_effort: "none"`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Uptime

30-Day Uptime

96.70%

Past Incidents (30d)

Error rate (24h)

9.27%

Last 30 days

26/30 days operational | 96.70% uptime

30 days ago Today

Operational Degraded Outage Maintenance

See All Incidents

GPT-5.2 in production

Best workloadsLLM.API features

What the model is good at.

Conversational Chat

Engages in multi-turn dialogue, following complex instructions, maintaining context, and producing coherent, relevant responses across many topics.
Text Translation

Translates between multiple languages, preserving meaning and tone while adapting to regional expressions and domain-specific terminology.
Visual Understanding

Interprets images to identify objects, scenes, and relationships, supporting tasks like description, comparison, and visual question answering.
Screen Interpretation

Understands and reasons about screen content such as interfaces, layouts, and structured documents to assist with navigation and analysis.
Document OCR

Extracts and structures text from images or scanned documents, enabling search, editing, and analysis of visual text content.

What you get when you call GPT-5.2 through LLM.API.

Unified AI Routing

Dynamically route each request across providers and models based on latency, cost, or performance—without changing your integration. Optimize behavior in code, not configs.
One endpoint, every model
Cost-Aware Orchestration

Control spend with smart model selection, rate limits, and cost ceilings per project. See and tune tradeoffs between price and quality in real time.
Max performance, minimal spend
Automatic Provider Fallback

When a model or provider fails, LLM.API transparently retries or reroutes to healthy alternatives—no manual failover logic, no downtime for your users.
Resilience by default
Deep LLM Observability

Capture traces, logs, metrics, and payloads for every call across providers. Debug prompts, compare models, and ship reliable AI features with production-grade visibility.
See every token
Task-Level Abstractions

Describe tasks—chat, RAG, tools, structured outputs—once and let LLM.API pick the right models and parameters. Evolve your stack without rewriting application code.
Think in tasks, not models
High-Throughput Batch

Submit large batches of prompts for offline or async processing with built-in deduping, retries, and cost controls. Scale evaluations, content generation, and backfills easily.
Millions of calls, one pipeline

When to Use — When NOT to Use

Use it if...

You need state-of-the-art reasoning and general-purpose intelligence across diverse complex tasks.
You need strong performance on code generation, debugging, and multi-file software refactoring.
You need high-quality natural language understanding and generation for chatbots and agents.
Your use case involves complex data analysis, synthesis, and explanation for non-experts.
Your use case involves multi-step tool usage and orchestration within larger AI systems.
You need a single versatile model for text, reasoning, and light code workloads.

Avoid if...

You need the absolute lowest-cost model for simple, repetitive, template-based outputs.
Your workload requires ultra-low latency responses on edge devices with limited compute.
You need strict on-prem deployment with no external API dependencies or connectivity.
Your workload requires only basic text classification where smaller models perform similarly.
You need deterministic, fully auditable rule-based behavior instead of probabilistic generation.
Your workload requires heavy multimedia generation better served by specialized vision or audio models.

LLM API rating for GPT-5.2

★ 2.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

2.3Production adoptionRanked #130 on the public LMArena leaderboard

3.3Benchmark qualityArtificial Analysis Intelligence Index 30.4 · LMArena text rank #130, Elo 1412

2.9Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 2.7Coding 2.7Vision & multimodal 2.8Long context 3.0

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about GPT-5.2

LMArena#130 · Elo 1412Arena coding#128 · Elo 1432Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

Developers on GitHub report GPT-5.2 is a superior workhorse for professional work, praising its consistent performance, profound attention improvements, and ability to handle large-scale refactoring tasks autonomously overnight.
Users on Hacker News highlight that GPT-5.2 and its Codex variant excel at methodical bug detection and code review, often identifying serious issues that competing models like Claude fail to catch.
Some developers express frustration with the GPT-5.2 Codex variant, claiming it is overly concise and underperforms compared to the base GPT-5.2 model during complex agent-based planning and implementation workflows.

SOURCES

Frequently Asked Questions

What is GPT-5.2?

GPT-5.2 is a large multimodal OpenAI model accessible via LLM.API, designed for advanced reasoning, coding, and content generation across text and image inputs.
What modalities does GPT-5.2 support through LLM.API?

GPT-5.2 supports text input and output, and can optionally process image inputs when invoked with the appropriate LLM.API parameters.
How is GPT-5.2 priced when used via LLM.API?

LLM.API meters GPT-5.2 usage based on tokens processed, with per-input and per-output token rates defined in LLM.API’s pricing documentation.
What is the context window of GPT-5.2?

GPT-5.2 supports a large-context window suitable for long conversations and multi-file prompts; check LLM.API docs for the exact current token limit.
How fast is GPT-5.2 in terms of latency and throughput?

GPT-5.2 is optimized for low latency and streaming responses, but actual speed depends on prompt size and concurrent load on LLM.API.
How do I call GPT-5.2 via LLM.API?

You select the GPT-5.2 model name in your LLM.API request payload, include your LLM.API key, then send standard chat or completion-style requests.
What is GPT-5.2 particularly good at?

GPT-5.2 excels at complex multi-step reasoning, code generation and refactoring, long-form writing, and following detailed instructions across domains.
How does GPT-5.2 compare to earlier OpenAI models like GPT-4.1?

GPT-5.2 generally offers stronger reasoning, better instruction following, and more robust handling of long context than GPT-4.1 at comparable usage patterns.
What limitations should I be aware of when using GPT-5.2?

GPT-5.2 can still hallucinate facts, misinterpret ambiguous instructions, and should not be used as the sole source for high-stakes decisions.
Can I fine-tune GPT-5.2 through LLM.API?

Fine-tuning support for GPT-5.2 depends on LLM.API’s current feature set; check their documentation for whether fine-tuning is enabled for this model.

COMPARE

Competitive Models

GPT-5.4 is an OpenAI language model, but as of now OpenAI has not publicly released technical details or documentation about this specific version, so only its name and provider are known.

GPT-4o Mini Transcribe

Speech-to-Text

GPT-4o Mini Transcribe is an OpenAI model specialized for converting spoken language in audio into accurate text. It is optimized for lightweight, fast transcription while maintaining good recognition quality across common speech scenarios.

GPT-5.1-Codex-Mini

Up to 30%

Code Generation

GPT-5.1-Codex-Mini is an OpenAI code-focused model variant optimized for lightweight, fast software development assistance. It is notable for providing capable code generation and editing while using fewer resources than larger Codex-style models.

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GPT-5.2 API: Endpoint & 30% Discount

What is GPT-5.2?

Can you self-host GPT-5.2?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Uptime

GPT-5.2 in production

Conversational Chat

Text Translation

Visual Understanding

Screen Interpretation

Document OCR

Unified AI Routing

Cost-Aware Orchestration

Automatic Provider Fallback

Deep LLM Observability

Task-Level Abstractions

High-Throughput Batch