GPT-5 Pro is a flagship OpenAI large language model accessible via LLM.API, designed for advanced reasoning, coding, and complex multi-step workflows.

What is GPT-5 Pro best suited for?

GPT-5 Pro is best for production-grade agents, complex code generation and refactoring, data-heavy analysis, and high-quality natural language generation across many domains.

How is GPT-5 Pro priced when used through LLM.API?

GPT-5 Pro pricing on LLM.API is usage-based per input and output token; check your LLM.API dashboard or pricing docs for current rates.

What context window does GPT-5 Pro support?

GPT-5 Pro supports very long prompts and conversations with a large context window suitable for multi-document workflows; see LLM.API docs for exact token limits.

How fast is GPT-5 Pro in terms of latency and throughput?

GPT-5 Pro offers low latency suitable for interactive applications, with actual response times depending on request size, concurrency, and LLM.API routing conditions.

Which modalities does GPT-5 Pro support via LLM.API?

Through LLM.API, GPT-5 Pro supports text input and output, with optional image input and structured tool-calling depending on your integration configuration.

How do I call GPT-5 Pro through the LLM.API gateway?

Specify the GPT-5 Pro model name in your LLM.API request payload and authenticate with your LLM.API key; no direct OpenAI key is required.

How does GPT-5 Pro compare to earlier OpenAI models like GPT-4.1?

Compared to GPT-4.1, GPT-5 Pro generally provides stronger reasoning, better coding capabilities, and more reliable tool use at similar or better efficiency.

What limitations should I be aware of when using GPT-5 Pro?

GPT-5 Pro can still hallucinate, reflect training data biases, mis-handle ambiguous instructions, and should not be used without human oversight for high-stakes decisions.

Can GPT-5 Pro call tools or structured functions through LLM.API?

Yes, GPT-5 Pro supports tool and function calling when you define tools in your LLM.API configuration and enable structured outputs in requests.

Which endpoint do I call for GPT-5 Pro?

POST https://api.llmapi.ai/v1/chat/completions with model "gpt-5-pro" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to GPT-5 Pro 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 Pro 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 Pro API: Endpoint & Pricing

GPT-5 Pro is an OpenAI model, but as of mid-2026 OpenAI has not publicly released technical details, benchmarks, or official documentation about it.

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What is GPT-5 Pro?

GPT-5 Pro is an OpenAI AI language model name for which no official specifications or public documentation have been released. Because of this, there are no confirmed details about its primary use cases beyond general large language model tasks such as text generation, analysis, or assistance. Until OpenAI publishes authoritative information, its exact capabilities, domains of strength, and deployment contexts remain unknown. It is presumed—based on naming alone—to be related to the GPT model family, but its precise place in that lineage has not been formally defined.

Can you self-host GPT-5 Pro?

No. We found no public weights for GPT-5 Pro 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$15 in

$120 out

openai$15 in

$120 out

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

Provider	Input	Output	Cache read /M	Latency	Throughput	Uptime
azure	in $15; out $120 per 1M tokens	400K tokens	vision, tools, reasoning, web search, JSON, structured
openai	in $15; out $120 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 Pro right here — free to start.

GPT-5 Pro

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-pro 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-pro",
    "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-pro",
    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-pro",
  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-pro",
    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-pro",
  "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-pro · 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	Not 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 Pro in production

Best workloadsLLM.API features

What the model is good at.

Advanced Chat

Engages in multi-turn, context-aware conversations, following complex instructions and maintaining coherent dialogue across long interactions.
Code Monitoring

Analyzes logs or outputs to help monitor systems, reason about issues, and suggest improvements to technical setups or workflows.
Language Translation

Translates between many natural languages, preserving meaning and tone while adapting to different formality levels and contexts.
Image Analysis

Interprets image content, describing scenes and objects and supporting reasoning about visual details when such capability is available.
Document OCR

Extracts machine-readable text from images of documents or screenshots when optical character recognition functionality is provided.

What you get when you call GPT-5 Pro through LLM.API.

Unified AI Routing

Define intent-based routes once, then dynamically send traffic to the best model by cost, latency, or quality without changing your application code.
One endpoint, every model
Cost-Aware Orchestration

Automatically pick the most economical models for each request, enforce budgets, and track spend per team or feature so you never lose control of LLM costs.
Lower cost, same output
Resilient Fallback Flows

Configure multi-step fallbacks across providers so timeouts, rate limits, or model failures transparently recover without impacting your users or requiring manual rewrites.
Never fail on 500s
End-to-End Observability

Get complete visibility into prompts, latencies, errors, and provider behavior, with traceable logs for every request and route to debug production issues faster.
See every token hop
Task-Level Abstractions

Describe tasks like chat, extraction, or tool-calling once, and let LLM.API handle prompt patterns, model quirks, and response shaping across providers.
Code to tasks, not models
High-Throughput Batching

Batch thousands of calls into optimized requests with built-in retries and throttling, maximizing throughput while staying within provider limits and SLAs.
Scale from day one

When to Use — When NOT to Use

Use it if...

You need state-of-the-art reasoning and planning for complex, high-stakes decision workflows.
You need strong coding assistance, refactoring, and debugging across large multi-file repositories.
You need advanced natural language understanding for nuanced instructions, negotiation, and dialogue.
You need multimodal capabilities that combine text with images or other supported modalities.
Your use case involves building intelligent agents that autonomously orchestrate tools and APIs.
You need high-quality content generation, editing, and translation with consistent tone control.
Your use case involves complex data analysis, summarization, and synthesis from long documents.

Avoid if...

You need a fully offline model that can run entirely on local hardware.
You need the absolute lowest possible per-token cost for massive low-value traffic.
You need strict, deterministic outputs identical across time for regulatory certification workflows.
Your workload requires guaranteed hard real-time responses under 50 milliseconds end-to-end.
Your workload requires training or fine-tuning the base weights directly on proprietary data.
You need a model that supports unsupported languages or scripts with near-native fluency.
Your workload requires unrestricted access to disallowed content or unsafe prompt categories.

LLM API rating for GPT-5 Pro

★ 4.2/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

5.0Production adoptionTop 1% of production API volume

3.5Benchmark qualityNo public Arena rank — scored on declared reasoning support

3.1Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 4.2Coding 4.5Vision & multimodal 4.2Tool & function calling 4.6Long context 4.4

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 Pro

LMArena—Speed—Sources: LMArena, Artificial Analysis

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

Developers on the OpenAI community report GPT-5 Pro excels at initial debugging but can struggle with long coding threads, sometimes reverting correct changes or 'plateauing' compared to O3 Pro.
Some practitioners describe the model as 'jagged,' noting it provides institutional-grade analysis and high IQ scores while failing at creative writing or maintaining personality during conversations.
Developers using the API suggest that GPT-5 Pro's performance is significantly improved when using 'auditor agents' and JSON-based orchestration rather than simple code pasting.

SOURCES

Frequently Asked Questions

What is GPT-5 Pro?

GPT-5 Pro is a flagship OpenAI large language model accessible via LLM.API, designed for advanced reasoning, coding, and complex multi-step workflows.
What is GPT-5 Pro best suited for?

GPT-5 Pro is best for production-grade agents, complex code generation and refactoring, data-heavy analysis, and high-quality natural language generation across many domains.
How is GPT-5 Pro priced when used through LLM.API?

GPT-5 Pro pricing on LLM.API is usage-based per input and output token; check your LLM.API dashboard or pricing docs for current rates.
What context window does GPT-5 Pro support?

GPT-5 Pro supports very long prompts and conversations with a large context window suitable for multi-document workflows; see LLM.API docs for exact token limits.
How fast is GPT-5 Pro in terms of latency and throughput?

GPT-5 Pro offers low latency suitable for interactive applications, with actual response times depending on request size, concurrency, and LLM.API routing conditions.
Which modalities does GPT-5 Pro support via LLM.API?

Through LLM.API, GPT-5 Pro supports text input and output, with optional image input and structured tool-calling depending on your integration configuration.
How do I call GPT-5 Pro through the LLM.API gateway?

Specify the GPT-5 Pro model name in your LLM.API request payload and authenticate with your LLM.API key; no direct OpenAI key is required.
How does GPT-5 Pro compare to earlier OpenAI models like GPT-4.1?

Compared to GPT-4.1, GPT-5 Pro generally provides stronger reasoning, better coding capabilities, and more reliable tool use at similar or better efficiency.
What limitations should I be aware of when using GPT-5 Pro?

GPT-5 Pro can still hallucinate, reflect training data biases, mis-handle ambiguous instructions, and should not be used without human oversight for high-stakes decisions.
Can GPT-5 Pro call tools or structured functions through LLM.API?

Yes, GPT-5 Pro supports tool and function calling when you define tools in your LLM.API configuration and enable structured outputs in requests.

COMPARE

Competitive Models

GPT-4o Transcribe is an OpenAI model specialized for converting audio into accurate, time-aligned text transcripts. It is notable for handling natural speech, varied accents, and real-world audio conditions with high reliability.

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.

GPT-5.4

Up to 30%

Text Generation

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.

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GPT-5 Pro API: Endpoint & Pricing

What is GPT-5 Pro?

Can you self-host GPT-5 Pro?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Uptime

GPT-5 Pro in production

Advanced Chat

Code Monitoring

Language Translation

Image Analysis

Document OCR

Unified AI Routing

Cost-Aware Orchestration

Resilient Fallback Flows

End-to-End Observability

Task-Level Abstractions

High-Throughput Batching