Cognitivecomputationsbalanced

Uncensored (free)

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 user control over alignment, system prompts, and behavior. Intended for advanced and unrestricted use cases, Venice Uncensored emphasizes steerability and transparent behavior, removing default safety and alignment layers typically found in mainstream assistan

33K context·~25K words·
Context window33Ktokens

Context window

This model accepts 33K tokens in one request (~25K words of text).

Context window size33K tokens
4K32K128K1M10M

What fits in one request

  • Short document
    About 1,500 words of text
    Fits
  • Long document
    About 37K words of text
    Won't fit
  • Small codebase
    About 150K words of text
    Won't fit
  • Full novel
    About 375K words of text
    Won't fit

Specifications

Context size, pricing, and release info in one place.

Context window
32,768 tokens (33K)
Speed tier
balanced
Provider
Cognitivecomputations
Release date
Jul 2025

Capabilities

See which features this model supports, such as vision, tools, and streaming.

Supported (1)
Streaming
Supported
Not supported (7)
Vision
Not supported
Tool use
Not supported
Function calling
Not supported
Extended thinking
Not supported
Web search
Not supported
Batch API
Not supported
Prompt caching
Not supported

Best for

Jump to a guide or ranking that matches each workload.

Compare Uncensored (free)

Open a side-by-side comparison with one click.

Frequently asked questions

Short answers about context size and how this model behaves.

Uncensored (free) has a context window of 32K tokens (32,768 tokens). This is sufficient for most chat, summarization, and moderate document tasks.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model