LLMcognitivecomputations

Venice: Uncensored

Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving...

Anyone in the Project can @-mention Venice: Uncensored with the team's shared context - pooled credits, one chat, one memory.

All models

Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

Venice Uncensored is a 128K-context model built for teams that need minimal content filtering and fast turnaround on sensitive or controversial topics. It trades benchmark-proven accuracy for fewer guardrails, making it useful when standard models refuse requests or over-censor outputs. At $0.20/$0.90 per Mtok, it sits in the budget tier but lacks public benchmark data to validate reasoning or coding performance. Reach for this when you need a model that won't second-guess your prompts, but verify outputs carefully and consider pairing it with a higher-accuracy model for critical work.

Best for

  • Content moderation research and red-teaming
  • Generating edgy or controversial creative writing
  • Prototyping prompts that trigger standard guardrails
  • Low-stakes tasks where speed trumps accuracy

Strengths

Venice Uncensored removes most content policy restrictions, letting you explore prompts that would trigger refusals in GPT-4 or Claude. The 128K context window handles long documents or multi-turn conversations without truncation. Pricing undercuts frontier models by 5-10x, making it viable for high-volume experimentation. Response latency is competitive with other mid-tier models, so you won't wait long for outputs even on complex prompts.

Trade-offs

No public benchmarks means you're flying blind on reasoning, coding, and factual accuracy relative to peers like Llama 3.3 70B or Mistral Large. The lack of safety filters increases risk of harmful or biased outputs, requiring human review for any customer-facing use. Proprietary licensing limits transparency into training data and fine-tuning methods. Early user reports suggest weaker performance on math and structured data extraction compared to similarly priced alternatives.

Specifications

Provider
cognitivecomputations
Category
llm
Context length
128,000 tokens
Max output
8,192 tokens
Modalities
text
License
proprietary
Released
2025-07-09

Pricing

Input
$0.20/Mtok
Output
$0.90/Mtok
Model ID
cognitivecomputations/dolphin-mistral-24b-venice-edition

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Estimated monthly spend
$7.22
17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Red-Team a Chatbot

Generate 10 adversarial prompts designed to test content moderation systems. Include edge cases around medical advice, financial speculation, and controversial historical events. Format as a numbered list with brief rationale for each.
Open in a Project →

Unrestricted Creative Brief

Write a 200-word product description for a fictional energy drink marketed to extreme sports athletes. Use aggressive, boundary-pushing language that emphasizes risk and adrenaline. No corporate safety disclaimers.
Open in a Project →

Policy Gap Analysis

Describe how three different AI content policies (permissive, moderate, strict) would respond to a user asking for instructions to build a competitive gaming PC. Highlight where each policy draws the line.
Open in a Project →

Unfiltered Brainstorm

Brainstorm 15 unconventional marketing angles for a new dating app targeting Gen Z. Include provocative, humorous, and boundary-pushing concepts. Don't filter for brand safety yet—just generate ideas.
Open in a Project →

Sensitive Topic Summary

Summarize the key arguments on both sides of the debate around content moderation on social media platforms. Include perspectives that mainstream models often decline to articulate. Keep it factual and balanced, 300 words.
Open in a Project →

Compare with

More language models

See all language models

Data last verified 7 hours ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.