Uncensored
Cognitive Computations · released Jun 12, 2025 · cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
- Type
- Open weightsApache License 2.0
- Params
- 24B
- Context
- 128K
about 96K words of context
Our take
Written Sep 4, 2026Uncensored is a 24-billion-parameter text model from Cognitive Computations with a permissive Apache licence and a 128,000-token request limit. It is built for teams that need open weights and uncensored generation, though no benchmark scores verify its quality claims.
Pick this when uncensored output is a hard requirement and you want open weights with commercial freedom. Use it for self-hosted deployment where Apache 2.0 licensing matters, or budget-conscious text generation with identical rates across both tracked hosts. Skip it if you need measured quality scores, multimodal input, or guaranteed throughput on every provider.
The case for it
- Fully open Apache 2.0 licence allows commercial use, modification and redistribution.
- 128,000-token request limit is large for a 24-billion-parameter model.
- Identical pricing across both tracked providers, so switching host costs nothing extra.
The case against it
- No benchmark scores in our data, so quality is unverified.
- Active parameter count undisclosed — whether it uses a mixture-of-experts or dense design is unclear.
- Throughput only reported for one of two providers; the other is unverified.
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 5.7 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 13.7 GB spare means a 10% error in the size would not change the answer.
Apple M2 (10-core GPU) · 24 GB
Borderline fit on an estimated size. It leaves 0.9 GB spare on a size we calculated rather than measured, and a 10% error either way would change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 4 hours ago — each listing carries its own date.
- per 1M tokens
- $0.20 in / $0.90 out
- Context served
- 128K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.20 / $0.90checked 4 hours ago | 128K | not measured | Unknown | Unknown | Unknown |
| Venice AIfp16Through OpenRouter | $0.20 / $0.90checked 4 hours ago | 128K8K max reply | 40 tok/s | No | No | Confirmed |
Across the 2 listings we hold: 1 says it does not train on prompts, 0 say they do and 1 does not say. 1 appears in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✗ | ✓ | ✗ |
| Venice AIfp16Through OpenRouter | ✗ | ✓ | ✗ |
Tool calling: 0 of 2 listings say yes, 2 say no. JSON output: 2 of 2 listings say yes. Strict schema: 0 of 2 listings say yes, 2 say no.
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No independent board has scored it, so we hold no quality figures at all.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 2 listings does not say whether it trains on prompts.
- We hold no cached-input rate for any of its listings.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsApache License 2.0, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Architecture
- Dense
- Takes in, gives back
- Text in, text out
- Catalogue slug
- cognitivecomputations-uncensored