GPT-5.1 is a frontier OpenAI model accessible via LLM.API, optimized for high-quality reasoning, coding, and multimodal interactions.

What modalities does GPT-5.1 support through LLM.API?

GPT-5.1 supports text input and output via LLM.API; check the LLM.API docs for current support of image, audio, or other modalities.

How is GPT-5.1 priced when used via LLM.API?

GPT-5.1 pricing is usage-based per input and output token, with exact rates defined in the LLM.API pricing documentation.

What is the context window of GPT-5.1?

GPT-5.1 supports a large token context window suitable for long conversations and documents; consult LLM.API docs for the current token limit.

How fast is GPT-5.1 in terms of latency?

GPT-5.1 typically returns first tokens within a few seconds, with total latency depending on prompt size, response length, and LLM.API routing.

What is GPT-5.1 best suited for?

GPT-5.1 is best for complex reasoning, advanced coding assistance, multi-step tool use, and high-quality natural language generation across domains.

How do I call GPT-5.1 through LLM.API?

Specify the model name "GPT-5.1" in your LLM.API request payload and authenticate with your LLM.API key as described in the API docs.

How does GPT-5.1 compare to earlier OpenAI models like GPT-4.1?

GPT-5.1 generally improves on reasoning depth, coding reliability, and instruction following compared with GPT-4.1, while remaining API compatible via LLM.API.

What are the main limitations of GPT-5.1?

GPT-5.1 can still hallucinate facts, misunderstand ambiguous instructions, and lacks real-time access to proprietary or constantly changing external data by default.

Can I fine-tune or customize GPT-5.1 via LLM.API?

Fine-tuning or configuration options for GPT-5.1 depend on LLM.API’s current feature set; check the fine-tuning section of the documentation.

Which endpoint do I call for GPT-5.1?

POST https://api.llmapi.ai/v1/chat/completions with model "gpt-5.1" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to GPT-5.1 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-5.1 support function calling and structured outputs?

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

GPT-5.1 API: Endpoint & 30% Discount

Up to 30%Deprecated 1 Apr 2027

GPT-5.1 is an OpenAI language model; as of mid-2026, OpenAI has not publicly released technical details or documentation about it.

Start Using API

What is GPT-5.1?

GPT-5.1 is an OpenAI model name for which no official public specification, capabilities overview, or documentation has been released as of mid-2026. Because of this, there is no reliable, verifiable information about its intended primary use cases beyond general large-language-model tasks like text generation, coding assistance, and reasoning that OpenAI models typically target. Any more specific claims about its performance, architecture, or domain specialization would be speculative and are not supported by public sources. It is presumably related in name to the GPT model family that includes earlier generations such as GPT-3.5, GPT-4, and GPT-4.1, but its exact position or role in that family has not been formally described.

Can you self-host GPT-5.1?

No. We found no public weights for GPT-5.1 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$1.25 in

$10 out

openai$1.25 in

$10 out

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

Provider	Input	Output	Cache read /M	Latency	Throughput	Uptime
azure30% off	in $1.25; out $10 per 1M tokens	400K tokens	vision, tools, streaming, reasoning, web search, JSON, structured
openai	in $1.25; out $10 per 1M tokens	400K tokens	vision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test GPT-5.1 right here — free to start.

GPT-5.1

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-5.1 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-5.1",
    "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-5.1",
    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-5.1",
  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-5.1",
    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-5.1",
  "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-5.1 · 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	`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-5.1 in production

Best workloadsLLM.API features

What the model is good at.

Advanced Chat

Engages in multi-turn conversations, following complex instructions and maintaining context across long interactions for varied assistant-style tasks.
Image Understanding

Interprets and reasons about images, supporting tasks like description, comparison, and extraction of visual details from user-provided pictures.
Text Translation

Translates between many languages while preserving meaning and tone, supporting instructions to constrain or adapt style as needed.
Document OCR

Extracts text and structure from images or scans of documents, enabling downstream search, summarization, and analysis workflows.
Usage Monitoring

Supports integration into applications where developers can observe, evaluate, and iterate on prompts and outputs for quality control.

What you get when you call GPT-5.1 through LLM.API.

Intelligent Model Routing

Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model
Cost-Aware Orchestration

Enforce budgets, cap spend per app or tenant, and downshift to cheaper models automatically—so you control cost without manually tuning every call.
Predictable AI spend
Resilient Fallback Flows

Define fallback chains so if a model, region, or provider fails, calls transparently fail over without breaking your application or SLAs.
No single point of failure
End-to-End Observability

Get unified logs, metrics, traces, and per-provider analytics so you can debug issues, tune routing, and track performance from a single pane.
See every token, everywhere
Task-Level Abstractions

Use high-level task APIs—chat, generation, tools, embeddings—instead of provider-specific formats, so you can swap models without rewriting business logic.
Code to tasks, not vendors
High-Throughput Batch Jobs

Run large-scale batch inference across models and providers with automatic sharding, retries, and progress tracking to keep pipelines fast and reliable.
Scale inference on autopilot

When to Use — When NOT to Use

Use it if...

You need a general-purpose model that balances strong reasoning, coding, and language skills.
You need high-quality natural language understanding and generation for chatbots or virtual assistants.
Your use case involves building chat assistants that must handle diverse, unpredictable queries.
You need high-quality code generation, refactoring, and debugging across multiple programming languages.
Your use case involves complex natural language understanding, such as contract or policy review.
You need a single model that performs well across text, tools, and structured outputs.

Avoid if...

You need the absolute lowest inference cost and can accept noticeably weaker model quality.
Your workload requires ultra-low latency responses for tight real-time or on-device interactions.
You need guaranteed offline or fully self-hosted deployment without relying on cloud services.
Your workload requires strict, custom fine-tuning beyond what OpenAI’s tooling currently supports.
You need a model optimized solely for simple classification where smaller models suffice.
Your workload requires full transparency into weights and training data, including complete open weights.

LLM API rating for GPT-5.1

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

2.3Production adoptionRanked #105 on the public LMArena leaderboard

3.1Benchmark qualityArtificial Analysis Intelligence Index 24.7 · LMArena text rank #105, Elo 1422

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-5.1

LMArena#105 · Elo 1422Arena coding#117 · Elo 1437Speed—Sources: LMArena, Artificial Analysis

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

A developer at SomethingBig.ai describes the model as a powerful reasoning tool that follows complex instructions precisely, acting more like a contract engineer than a simple assistant for serious coding tasks.
Developers report that GPT-5.1 Pro excels at deep thought and research tasks where accuracy is critical, though its slow response times make it less ideal for fast, day-to-day iterations.
Concerns were raised regarding the user experience, with developers noting that the model's confinement to the ChatGPT interface rather than IDE integration creates significant friction and workflow limitations.

SOURCES

SomethingBig.ai — My GPT-5.1 Pro Review: An Absolute Monster

Frequently Asked Questions

What is GPT-5.1?

GPT-5.1 is a frontier OpenAI model accessible via LLM.API, optimized for high-quality reasoning, coding, and multimodal interactions.
What modalities does GPT-5.1 support through LLM.API?

GPT-5.1 supports text input and output via LLM.API; check the LLM.API docs for current support of image, audio, or other modalities.
How is GPT-5.1 priced when used via LLM.API?

GPT-5.1 pricing is usage-based per input and output token, with exact rates defined in the LLM.API pricing documentation.
What is the context window of GPT-5.1?

GPT-5.1 supports a large token context window suitable for long conversations and documents; consult LLM.API docs for the current token limit.
How fast is GPT-5.1 in terms of latency?

GPT-5.1 typically returns first tokens within a few seconds, with total latency depending on prompt size, response length, and LLM.API routing.
What is GPT-5.1 best suited for?

GPT-5.1 is best for complex reasoning, advanced coding assistance, multi-step tool use, and high-quality natural language generation across domains.
How do I call GPT-5.1 through LLM.API?

Specify the model name "GPT-5.1" in your LLM.API request payload and authenticate with your LLM.API key as described in the API docs.
How does GPT-5.1 compare to earlier OpenAI models like GPT-4.1?

GPT-5.1 generally improves on reasoning depth, coding reliability, and instruction following compared with GPT-4.1, while remaining API compatible via LLM.API.
What are the main limitations of GPT-5.1?

GPT-5.1 can still hallucinate facts, misunderstand ambiguous instructions, and lacks real-time access to proprietary or constantly changing external data by default.
Can I fine-tune or customize GPT-5.1 via LLM.API?

Fine-tuning or configuration options for GPT-5.1 depend on LLM.API’s current feature set; check the fine-tuning section of the documentation.

COMPARE

Competitive Models

GPT-5.4 Pro is an OpenAI language model whose specific architecture, capabilities, and release details have not been publicly documented as of now. Any concrete claims about its performance or features beyond official OpenAI announcements would be speculative.

GPT-5.4 Nano

Up to 30%

Text Generation

GPT-5.4 Nano is an OpenAI model name, but there is no public, reliable information available describing its architecture, capabilities, or intended use. Any additional details would be speculative.

GPT-5.5

Up to 30%

Instruction Following

GPT-5.5 is an OpenAI model; as of mid-2026, OpenAI has not publicly released technical details or documentation about this specific version.

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GPT-5.1 API: Endpoint & 30% Discount

What is GPT-5.1?

Can you self-host GPT-5.1?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Uptime

GPT-5.1 in production

Advanced Chat

Image Understanding

Text Translation

Document OCR

Usage Monitoring

Intelligent Model Routing

Cost-Aware Orchestration

Resilient Fallback Flows

End-to-End Observability

Task-Level Abstractions

High-Throughput Batch Jobs