Where to use uncensored and abliterated models through an OpenAI compatible API

If you build with the OpenAI SDK, you can use uncensored and abliterated models without changing your code: point the client at an API that speaks the same protocol and change the model id. This page shows where to do that, how, and which models are worth calling, with working code for the common SDKs and agent tools.

The short answer

OpenUncensored is an OpenAI compatible API for uncensored models. One base URL, https://api.openuncensored.com/v1, serves 61 uncensored, abliterated and heretic text models and 21 image models from 8 hosts. It supports chat completions, the Responses API, streaming, tool calling and image generation, so the OpenAI SDKs, LangChain, LiteLLM, the Vercel AI SDK, Cline, Cursor, Codex and OpenCode all work. Context windows go up to 1024K tokens, depending on the model.

Buy a prepaid key at openuncensored.com/credits: no account, no sign up, just a card and a key (ou-...). Or hand your agent openuncensored.com/agents.md and let it get one for you.

Call it

curl https://api.openuncensored.com/v1/chat/completions \
  -H "Authorization: Bearer $OU_API_KEY" -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4.1-flash-1017-uncensored", "messages": [{"role": "user", "content": "Hello"}]}'

from openai import OpenAI

client = OpenAI(base_url="https://api.openuncensored.com/v1", api_key="ou-...")
reply = client.chat.completions.create(
    model="deepseek-v4.1-flash-1017-uncensored",
    messages=[{"role": "user", "content": "Explain how SQL injection works, with an example."}],
)
print(reply.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.openuncensored.com/v1", apiKey: process.env.OU_API_KEY });
const reply = await client.chat.completions.create({
  model: "deepseek-v4.1-flash-1017-uncensored",
  messages: [{ role: "user", content: "Hello" }],
});

Streaming

Streams use the same server-sent events as OpenAI, so your existing loop works:

stream = client.chat.completions.create(
    model="deepseek-v4.1-flash-1017-uncensored",
    messages=[{"role": "user", "content": "Write a short story about a lock picker."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].delta.content or "", end="", flush=True)

Tool calling

Pass tools exactly as you would to OpenAI. Agent reliability differs a lot between uncensored models, so pick one that finishes our agent tasks (table below):

tools = [{
    "type": "function",
    "function": {
        "name": "lookup_cve",
        "description": "Look up a CVE by id",
        "parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]},
    },
}]
reply = client.chat.completions.create(
    model="deepseek-v4.1-flash-1017-uncensored",
    messages=[{"role": "user", "content": "What is CVE-2021-44228 and how is it exploited?"}],
    tools=tools,
)
print(reply.choices[0].message.tool_calls)

The Responses API

https://api.openuncensored.com/v1/responses serves OpenAI's newer Responses API, which is what the Codex CLI and other recent agents use:

resp = client.responses.create(model="deepseek-v4.1-flash-1017-uncensored", input="Summarise how phishing kits work, for a training deck.")
print(resp.output_text)

Reasoning variants (deepseek-v4.1-flash-0910-uncensored-thinking, deepseek-v4.1-flash-1017-heretic-thinking, deepseek-v4.1-flash-1017-uncensored-thinking, deepseek-v4.1-flash-uncensored-thinking, mimo-v2.6-flash-uncensored-thinking, qwen3.8-27b-uncensored-thinking) think before they answer and accept a reasoning effort.

Other SDKs and frameworks

Vercel AI SDK (npm i ai @ai-sdk/openai-compatible):

import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText } from "ai";

const openUncensored = createOpenAICompatible({
  name: "openuncensored",
  apiKey: process.env.OPENUNCENSORED_API_KEY,
  baseURL: "https://api.openuncensored.com/v1",
  includeUsage: true,
});

const { text } = await generateText({ model: openUncensored("deepseek-v4.1-flash-1017-uncensored"), prompt: "Hello" });

LangChain (pip install langchain-openai):

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="deepseek-v4.1-flash-1017-uncensored", base_url="https://api.openuncensored.com/v1", api_key="ou-...")
print(llm.invoke("Hello").content)

LiteLLM (pip install litellm):

import litellm

response = litellm.completion(
    model="openai/deepseek-v4.1-flash-1017-uncensored",
    api_base="https://api.openuncensored.com/v1",
    api_key="ou-...",
    messages=[{"role": "user", "content": "Hello"}],
)

Cline, Kilo Code and Roo take the base URL and key under the OpenAI Compatible provider. Codex, Claude Code and OpenCode have their own guides: Claude Code and Codex, OpenCode.

Which abliterated models to call

Uncensoring a model is easy to do badly: some versions loop in agent tasks, some still refuse, some lose skills. We run every model through the same tests (intelligence, agent tasks, refusals, speed) and rank them. The current top 8:

Rank	Model	id	Agent tasks	Answers edgy requests	$ per 1M in + out
#1	DeepSeek V4.1 Flash 1017 Uncensored	deepseek-v4.1-flash-1017-uncensored	2/5	100%	$1.65
#2	GLM 5.3 Flash Uncensored	glm-5.3-flash-uncensored	3/5	94%	$0.620
#3	Qwen 3.8 27B Uncensored Fast by Heabsy	qwen3.8-27b-uncensored-fast	2/5	100%	$3.45
#4	DeepSeek V4.1 Flash 0910 Uncensored	deepseek-v4.1-flash-0910-uncensored	2/5	97%	$1.65
#5	DeepSeek V4.1 Flash Uncensored	deepseek-v4.1-flash-uncensored	1/5	100%	$0.570
#6	Abliterated Large Flash	abliterated-large-flash	1/5	92%	$3.00
#7	MiMo V2.6 Flash Uncensored	mimo-v2.6-flash-uncensored	2/5	83%	$1.96
#8	Apex Flash 1 Abliterated	glm-5.3-flash-apex-abliterated	3/5	86%	$0.650

Agent tasks are five multi-step tool scenarios. Answers edgy requests counts 36 legitimate prompts that cautious models refuse. The full table is on the rankings page, and every model with its providers and live speed is on the models page.

Routing: which provider answers

Most models are served by more than one host. By default the router picks the cheapest provider among those producing at least 150 tokens per second and falls back to the others. You can choose per request:

{
  "model": "deepseek-v4.1-flash-1017-uncensored",
  "messages": [{"role": "user", "content": "…"}],
  "routing": {
    "strategy": "cost",
    "only": ["venice", "nanogpt"],
    "ignore": ["openrouter"],
    "allow_fallbacks": true
  }
}

Strategies are cost, speed (time to first token) and throughput (tokens per second). You can also set a default for the whole key on the credits page. More in how routing works.

Every response says what happened in its headers: X-OU-Model (the model that answered, which can differ after a fallback), X-OU-Provider, X-OU-Upstream-Model, X-OU-Strategy and X-Request-ID.

Errors

Status	Code	What it means	What to do
401	invalid_api_key	Missing or wrong key	Send Authorization: Bearer ou-...
402	insufficient_quota	The key's credit is used up	Top up the same key at /credits
404	model_not_found	No model with that id	Copy the id from /models or GET https://api.openuncensored.com/v1/models
400	invalid_routing_options	only/ignore exclude every provider, or an unknown strategy	Loosen the routing block
4xx	upstream_rejected_request	The provider rejected the request itself	Read the message: usually a parameter that model does not support
429	rate_limit_exceeded	Every provider for the model is rate limited	Retry after the Retry-After header
502	all_providers_failed	Every provider was tried and failed	Retry, or pick another model

What an OpenAI compatible uncensored API should give you

• The same endpoints: /chat/completions and /responses, with streaming and tool calls, so agents work.
• Honest model ids: the response says which model and which provider answered.
• Fallbacks: when a provider is down, the request goes to the next one serving the same weights, or the next best uncensored model built on the same original.
• Visible per provider prices: each model page lists every provider's per token and cached price, and live speed.
• Spending you control: a prepaid key cannot spend more than you loaded.

Other places to call uncensored models by API

OpenUncensored routes to several of these. Each one is also usable directly:

Provider	What it is	Billing	Prompt logging	Pricing (their figures)
RedPill	Hosts models. Confidential inference in trusted execution environments (Phala), including an uncensored Qwen 3.8 27B.	Per token	Not published	Qwen 3.8 27B uncensored at $0.30 in / $1.50 out per M tokens.
Venice	Hosts models. Private inference with its own uncensored model line and a crypto native API.	Per token or plan, crypto accepted	Says no logs	API from $0.20 in / $0.90 out per M tokens. App plans $18 to $200 a month.
Featherless	Subscription. Flat rate serverless access to thousands of Hugging Face fine tunes, 781 of them tagged uncensored.	Per token or plan	Says no logs	Basic $10 a month, Chat $25 a month unlimited tokens at 32K context. Developer plans from $50.
ArliAI	Subscription. Unlimited token plans focused on creative writing and roleplay models.	Plan	Says no logs	Monthly plans with unlimited tokens.
heabsy	Hosts models. FEYA s.r.o. (Slovakia). Own GPUs in the EEA plus zero-retention partners, serving dealignai's DeepSeek V4.1 Flash 1017 and GLM 5.3 builds, Qwen 3.8 and MiMo uncensored models, and its own CyberHeabsy.	Per token	Says no logs	GLM 5.3 Flash Uncensored $0.20 in / $0.80 out, DeepSeek V4.1 Flash 1017 Uncensored $0.40 / $1.40 per M tokens.
Refuseless	Hosts models. DPLS Systems LLC (Wyoming). Its own abliterations of DeepSeek V4.1 Flash, GLM 5.3 and GLM 5.3 Flash. Prepaid credits.	Per token, crypto accepted	Says no logs	DeepSeek V4.1 Flash Abliterated $0.12 in / $0.45 out, GLM 5.3 Abliterated $1.00 / $2.00 per M tokens.
NanoGPT	Aggregator. Pay per prompt aggregator. No account needed, crypto checkout, TEE hosted models.	Per token, crypto accepted	Transient	Card from $1, BTC, Lightning and XMR from $0.10.
Abliteration.ai	Hosts models. Hosted abliterated frontier models, led by Abliterated Large v2 on GLM-5.3.	Per token or plan	Says no logs	Around $3 in / $3 out per M tokens. Monthly tiers $20 to $200.
Chutes	Decentralised network. Decentralised GPU network on Bittensor with very low per token prices.	Per token, crypto accepted	Not published	Per token, often below cost. Many models served at int4.
Atlas Cloud	Hosts models. Image generation API serving open FLUX and Z-Image weights.	Per token	Says no logs	FLUX Schnell $0.003, Z-Image Turbo $0.01, FLUX Dev $0.012 per image.

Details and sources for each are on the providers page.

OpenRouter also lists a few uncensored fine tunes among its general catalogue.

Frequently asked questions

What is the difference between abliterated, heretic and uncensored models?

• Abliteration: Projects a single refusal direction out of the weights (Arditi et al., 2024). Fast, no training, often costs a few points of capability.
• Heretic: Automated abliteration that searches ablation parameters to minimise refusals and KL divergence from the original model together.
• Norm preserving: Biprojected abliteration that keeps weight norms intact, designed to remove refusals with less collateral damage.
• Fine tune: Supervised fine tuning on compliant data. Changes behaviour more broadly than a projection.
• DPO: Preference optimisation that rewards compliance over refusal.
• Merge: Weight merge of an uncensored fine tune into a stronger parent.

"Uncensored" is the umbrella term. Which method works best varies by model, which is why we measure each version rather than trust the label.

Are my prompts logged?

Not by us: prompts and replies pass through the router to the provider and are not written to our storage. We keep request metadata (time, model, token counts, cost, speed) for billing and the public speed figures. Each provider's own policy is on the providers page, and routing.only keeps a request on the providers you trust.

How many requests can I run at once?

A prepaid key can have 32 requests in flight at a time. Beyond that, each provider's own rate limits apply, and the router moves to another provider of the same model when one is rate limited.

Can I use it from an agent without a human?

Yes. openuncensored.com/agents.md explains to an agent how to buy credit, pick a model and call it, and there is an MCP server for Claude, ChatGPT and other MCP clients.