Venice AI API alternative for uncensored security work

If you use Venice's API mostly for its uncensored models, the most useful comparison starts with the one model both of us list, DeepSeek V4.1 Flash Uncensored, and the one Venice doesn't list at all, an abliterated GLM-5.3 Flash. Venice AI runs a consumer chat app and an OpenAI-compatible developer API over a large multimedia catalog. We run an open source AI gateway backed by Y Combinator, and alongside 1,154 standard models we host two abliterated open models on our own GPUs as an uncensored LLM API for security teams. Everything below is taken from both companies' docs as of 27 October 2026.

Venice is the stronger pick when you want contract-enforced zero retention on DeepSeek V4.1 Flash Uncensored, TEE or end-to-end encrypted inference, image and video generation, or more uncensored models to choose from. Experiential makes more sense if you want abliterated GLM-5.3 Flash, a slightly lower list price on DeepSeek, or one key for the uncensored models and the GPT, Claude and Gemini families.

Venice and Experiential at a glance

The model both APIs list: DeepSeek V4.1 Flash Uncensored

Per million tokens, at list price:

Our list price is about 9% lower on fresh input and on output, and DeepSeek is 25% off list during the current promotion. Venice's cached-input price is far below ours, so an agent that resends a long context on every turn could cost less on Venice, depending on how much of each request hits the cache.

The shared name doesn't tell you the weights are the same. Ours is dealignai's uncensored build of DeepSeek-V4.1-Flash-1017, published on Hugging Face, with 1.05M context, 393K max output and text-only input, and dealignai's card shows its MMLU falling from 87.63% to 83.77% after uncensoring. Venice doesn't say who uncensored its version or which weights it runs, and its table lists vision input for it, so treat the two as possibly different models and run your own prompts through both.

Venice tags its version Private, which its docs define as contract-enforced zero data retention, with prompt and response content processed for inference only. If your team needs that guarantee in writing, Venice is the better fit for this model.

On our side, thinking is on by default at the high reasoning level (none, low, high and max are available), and the reasoning text comes back with the answer. As our docs put it, "A reasoning model spends part of max_tokens thinking", so set max_tokens with that in mind.

The model only we list: abliterated GLM-5.3 Flash

Venice lists Z.ai's stock GLM 5.3 Flash at $0.15 / $0.50, the same price as our GLM-5.3 Flash Uncensored, but no uncensored build of it. Ours runs dealignai's GLM-5.3-Flash-UNCENSORED-FP8 weights, from a 320B-total, 18B-active hybrid MoE that Z.ai released in late August 2026. It has 1M context and 131K max output, supports tool calls and structured output, and takes text input. MMLU is 87.3 against 86.7 for the base, and cached input is $0.06 per million tokens.

Both of our models are abliterated, meaning the refusal direction was removed from the weights. People also search for this as "obliterated models", and how abliteration works covers the method.

Venice's uncensored lineup

Venice's model table tags 14 text models Uncensored. They include its own Venice Uncensored 1.2 (128K context, $0.20 / $0.90), Gemma 4 Uncensored (256K, $0.1625 / $0.50), Qwen 3.6 Plus Uncensored (1M, $0.625 / $3.75), GLM 4.7 Flash Heretic (200K, $0.07 / $0.40), four Aion models, and Abliteration.ai's Abliterated Large Flash and Abliterated Large V2, which Venice resells. The table doesn't say who uncensored each model. If Abliteration.ai's own API is on your list, we cover it in Experiential vs Abliteration.ai.

Privacy differs across the lineup. Venice Uncensored 1.2 and Gemma 4 Uncensored are tagged Private along with DeepSeek. Qwen 3.6 Plus Uncensored and both Abliteration.ai models are tagged Anonymized, which means Venice proxies the request without passing your Venice identity to the provider, though the provider still sees the prompt.

How private each API is

Venice's privacy docs say it "does not store or log prompt and response content for normal inference". Beyond the Private and Anonymized tags, some models run in a trusted execution environment with remote attestation, and with E2EE your client encrypts the prompt so only the verified enclave can decrypt it. Venice does keep operational metadata, which its docs list as including key IDs, timestamps, token counts, billing amounts and IP address.

Our uncensored models keep every prompt and response even when storage is turned off. Experiential Labs keeps a copy after an account is deleted and no uncensored route is zero retention. The billing ledger keeps token counts, cost and timing with no prompt text. Traffic never trains shared models. We don't document a TEE or end-to-end encrypted option. The full rules are in our data controls doc.

Porting code from the Venice API

Both APIs take OpenAI-style chat completions with streaming, function calling and JSON-schema structured output. Venice also has a Responses API in beta and endpoints for images, audio, video and embeddings, and we serve OpenAI Chat Completions and Responses plus Anthropic Messages. A Chat Completions client needs three edits:

1from openai import OpenAI2import os3 4client = OpenAI(5    base_url="https://api.experientiallabs.ai/v1",  # was https://api.venice.ai/api/v16    api_key=os.environ["EXPLABS_API_KEY"],          # was VENICE_API_KEY7)8 9resp = client.chat.completions.create(10    model="deepseek-v4.1-flash-uncensored",         # was deepseek-v4-1-flash-uncensored11    messages=[{"role": "user", "content": "Explain what this captured request header does."}],12)13print(resp.choices[0].message.content)

Venice puts its own options in a venice_parameters object, so remove it from the request body. When our gateway meets a field it doesn't use, it either drops it and names it in the x-experiential-ignored-parameters response header or refuses the request with a 400, and that header is worth checking after your first calls. Venice's include_venice_system_prompt also defaults to true, which adds Venice-supplied system prompts alongside yours. Those stop when you leave, so make sure your own system prompt covers whatever you were relying on.

Keys, rate limits and terms of use

Creating a Venice key lets you choose an inference-only or admin key, set a consumption limit in USD or DIEM over a period or for the key's lifetime, set an expiry date, restrict the key to IP addresses or subnets, and set modelPrivacy so it can only call Private, TEE or E2EE models. There's no per-key list of allowed models.

Our keys each get a daily spend cap and requests-per-minute and tokens-per-minute limits. Budgets can be scoped to a team, a key or a single model, which lets you cap spending on an uncensored model by itself, and Enterprise adds SSO, private networking and data residency.

Billing works differently too. Venice draws from credits that its docs say "never expire" and lets you stake DIEM for a daily allowance (1 DIEM is documented as $1 a day of compute) or pay per request in USDC through x402 without an account. We charge per token against credits on Free, Pro ($20 a month), Max or Ultra, and teams with agents running all day can ask for committed pricing.

Venice's terms (updated 8 June 2026) bar illegal use, set a minimum age of 13, say Venice has no obligation to monitor content, and include no clause about security testing. We offer our uncensored models for authorized security work, meaning pen testing systems you have permission to test, defensive research and API reverse engineering, and our terms apply to every request. For why a security team needs these models in the first place, read why stock models refuse security work, or start with our guide to uncensored LLMs.

When to stay on Venice

• Contract-enforced zero retention on DeepSeek V4.1 Flash Uncensored is a requirement. Venice tags its version Private.
• You want TEE or end-to-end encrypted inference, which Venice documents and we don't.
• Image, video or audio generation should sit behind the same key as your text models.
• You'd like to compare more uncensored models. Venice tags 14 and we host two.
• IP allowlists, key expiry dates and a privacy-tier filter at key creation matter to your security review.
• You want to pay in crypto, by staking DIEM, or per request through x402 without an account.

Our two uncensored models and their live prices are on the uncensored LLM API page.