Atlas / Models / Cognitivecomputations / Venice: Uncensored

Venice: Uncensored✓ Catalog verified

cognitivecomputations/dolphin-mistral-24b-venice-edition

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...

Input price
$0.20 /1M
Output price
$0.90 /1M
Context
128K
Modalities
text
Released
Jul 9, 2025
Tool calling
Atlas signal
80/100
01

Overview

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...

Access: available through the official Cognitivecomputations API. Context window: 128K.

Pricing and metadata from the ModelsAtlas catalog.Last refreshed Aug 7, 2026
02

Specifications

API identifiercognitivecomputations/dolphin-mistral-24b-venice-edition
ProviderCognitivecomputations
Model typeText LLM
Context window128K catalog
Input modalitiestext
Output modalitiestext
ReleasedJul 9, 2025
ModeratedNo
Architecture modalitytext->text
Supported parameters
frequency_penaltymax_tokenspresence_penaltyresponse_formatstoptemperaturetop_ktop_p
Source: provider documentation + catalog feedMethodology →
03

Pricing

Live pricing components for cognitivecomputations/dolphin-mistral-24b-venice-edition as published in the catalog.

$0.20
Input /1M
$0.90
Output /1M
Per request
Per image
Pricing componentRaw unit priceNormalizedUnit
Prompt tokens$2e-7$0.20 / 1MPer input token
Completion tokens$9e-7$0.90 / 1MPer output token
Request feeN/AN/APer request
Image feeN/AN/APer image unit
Web search feeN/AN/APer search request
Source: catalog pricing feedCompare all pricing →Cheapest models →
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Cost calculator

Estimate monthly spend from your own token volumes.

Scale / Volume

Estimated Total Cost

$0.28Calculated from current list pricing in the ModelsAtlas catalog.
05

Capabilities

CapabilityStatusWhat it means
Visual UnderstandingNot advertisedImage and document analysis support
Audio ProcessingNot advertisedSpeech and voice aligned flows
Tool CallingNot advertisedSupports tools / function calling
Self-HostingNot advertisedDeploy outside managed APIs
Derived from catalog capability tags and supported parameters
06

Capability signals

Directional signals derived from model metadata and capability tags — not official benchmark submissions.

Venice: Uncensored
MMLU Signal (Reasoning)
79signal
Coding Signal (HumanEval proxy)
82signal
Math Signal (GSM8K proxy)
79signal
Science Signal (GPQA proxy)
79signal
Estimated from metadata — not an official benchmark runAll benchmarks →Methodology →
07

Quick start

Call Venice: Uncensored through an OpenAI-compatible client.

from openai import OpenAI

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

response = client.chat.completions.create(
    model="cognitivecomputations/dolphin-mistral-24b-venice-edition",
    messages=[{"role": "user", "content": "Explain quantum physics."}]
)

print(response.choices[0].message.content)
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Alternatives

Closest models by context window from a different provider.

09

Sources & attribution

Pricing and metadata are maintained in the ModelsAtlas catalog.

Capability bars use metadata tags — directional estimates onlyHow we source and verify data →
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Frequently asked questions

How much does Venice: Uncensored cost?

$0.20 per 1M input tokens and $0.90 per 1M output tokens on the official Cognitivecomputations API.

What is the context window of Venice: Uncensored?

Venice: Uncensored supports up to 128K tokens of context.

Does Venice: Uncensored support tool / function calling?

Venice: Uncensored does not advertise first-class tool/function calling. Check vendor docs for the latest capabilities.

How do I access Venice: Uncensored?

Use the official Cognitivecomputations API with the model id `cognitivecomputations/dolphin-mistral-24b-venice-edition`. See the Quick start section above for code examples.