Where is the canonical page for Custom Model?

https://llmapi.ai/models/custom/

Can I use tools or structured outputs with Custom Model?

Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

What modalities does Custom Model support?

Custom Model accepts text, image and produces text according to its architecture metadata on LLM.API.

Is Custom Model a chat model?

Yes—Custom Model is exposed as a chat/completions-style endpoint on LLM.API.

How do I call Custom Model via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "custom" and your LLM.API key. See the code snippet on this page.

What is the context length for Custom Model?

Reported context for Custom Model is See provider specs. Always verify the active provider row if multiple providers are listed.

How is Custom Model priced on LLM.API?

Listed pricing metadata shows: See LLM.API pricing. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

When should I choose Custom Model?

You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Custom Model.

Does Custom Model support streaming?

Yes—at least one listed provider advertises streaming.

What are limitations of Custom Model?

Like other API models, Custom Model can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

Which endpoint do I call for Custom Model?

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

How do I switch from OpenAI to Custom Model 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 Custom Model support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: not supported.

Custom Model API: Endpoint & Pricing

Call Custom Model through LLM.API when you want LLM.API-family text generation with unified auth, provider choice, and production-friendly defaults.

Start Using API

What is Custom Model?

Custom Model belongs to the LLM.API family and is offered as a hosted chat endpoint. custom provided by llmapi. It is wired for API access through LLM.API with OpenAI-compatible patterns. Through LLM.API you keep one base URL while selecting this model for assistants, tools, and content workflows.

Can you self-host Custom Model?

No. We found no public weights for Custom Model 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

LLM.API routes Custom Model to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

llmapi$0 in

$0 out

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

Provider	Pricing	Context	Capabilities
llmapi	in $0; out $0 per 1M tokens	—	vision, tools, streaming, JSON

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

Try this model

Test Custom Model right here — free to start.

Custom Model

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Custom Model through the OpenAI-compatible API — POST /v1/chat/completions.

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 custom 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": "custom",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="custom",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
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: "custom",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

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

stream = client.chat.completions.create(
    model="custom",
    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": "custom",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID custom · 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	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Supported	`image_url content parts`
Web search	Not supported	`web_search`

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.

Custom Model in production

Best workloadsLLM.API features

What the model is good at.

Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Grounded in the model's chat role rather than generic chat claims.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Grounded in the model's chat role rather than generic chat claims.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Reflects LLM.API positioning for this endpoint.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Grounded in the model's chat role rather than generic chat claims.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Tuned to how teams typically call Custom Model.

What you get when you call Custom Model through LLM.API.

Unified AI Routing
Production: Reach Custom Model and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Custom Model.
More performance, less spend.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for Custom Model alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Production: Swap Custom Model for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

You need a general-purpose text model for assistants, agents, or content workflows (Custom Model)
You want OpenAI-compatible chat completions through a single LLM.API key (Custom Model)
You need provider failover options exposed for this model id (Custom Model)
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Custom Model)

Avoid if...

You need guaranteed real-time hard latency SLAs without benchmarking the provider
Your use case depends on unpublished proprietary benchmarks not listed here
You need pure embedding, OCR, or media generation instead of chat
You require on-prem only deployment with no cloud inference

LLM API rating for Custom Model

★ 2.8/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

2.5Production adoptionNot yet tracked — no public usage data for this release

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

3.3Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 2.8Coding 2.6Vision & multimodal 2.8

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 Custom Model

LMArena—Speed—Sources: LMArena, Artificial Analysis

— No public developer feedback about this model yet.

Frequently Asked Questions

Where is the canonical page for Custom Model?
https://llmapi.ai/models/custom/
Can I use tools or structured outputs with Custom Model?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What modalities does Custom Model support?
Custom Model accepts text, image and produces text according to its architecture metadata on LLM.API.
Is Custom Model a chat model?
Yes—Custom Model is exposed as a chat/completions-style endpoint on LLM.API.
What is Custom Model?
custom provided by llmapi. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `custom`.
How do I call Custom Model via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "custom" and your LLM.API key. See the code snippet on this page.
What is the context length for Custom Model?
Reported context for Custom Model is See provider specs. Always verify the active provider row if multiple providers are listed.
How is Custom Model priced on LLM.API?
Listed pricing metadata shows: See LLM.API pricing. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
When should I choose Custom Model?
You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Custom Model.
Does Custom Model support streaming?
Yes—at least one listed provider advertises streaming.
What are limitations of Custom Model?
Like other API models, Custom Model can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

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Custom Model API: Endpoint & Pricing

What is Custom Model?

Can you self-host Custom Model?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Custom Model in production

Conversational UX

Safety-aware replies

Long-context synthesis

Multi-step reasoning

Analytical writing

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth