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Venice: Uncensored vs Qwen: Qwen3 VL 8B Instruct

A side-by-side comparison of Venice: Uncensored ($0.70 / 1M tokens) and Qwen: Qwen3 VL 8B Instruct ($0.88 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Venice: Uncensored	Qwen: Qwen3 VL 8B Instruct
Provider	Venice	Qwen
Input	$0.70 / 1M tokens	$0.88 / 1M tokens
Output	$3.15 / 1M tokens	$2.63 / 1M tokens
Cache read	-	$0.42 / 1M tokens
Context window	128,000	262,144
Max output	8,192	235,929
Input	text	image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 9, 2025	Oct 14, 2025

Key differences

Venice: Uncensored is 20% cheaper on input tokens.
Qwen: Qwen3 VL 8B Instruct is 17% cheaper on output tokens.
Qwen: Qwen3 VL 8B Instruct reads 2.0× more context (262,144 tokens).
Only Qwen: Qwen3 VL 8B Instruct supports tool calling.
Only Qwen: Qwen3 VL 8B Instruct supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00228 with Venice: Uncensored and $0.00219 with Qwen: Qwen3 VL 8B Instruct.

In depth

Venice: Uncensored is a text model from Venice, built on the Dolphin Mistral 24B lineage, and it was released on 2025-07-09. Qwen: Qwen3 VL 8B Instruct comes from Qwen and arrived on 2025-10-14. Both are listed under the text category on api14, so at the surface level they look like comparable chat completions endpoints, but they are aimed at different jobs. Venice is a single purpose text generator, while Qwen3 VL is a multimodal instruct model that also handles images on the input side.

The clearest split is in context and modalities. Qwen has the bigger context window at 262144 tokens against 128000 for Venice, so it can hold roughly twice as much material in a single call. Qwen also accepts image inputs alongside text, while Venice accepts text only; both return text. On tooling, Qwen is the more programmable of the two: it is the only one of the pair that supports tools and tool_choice, and the only one with structured_outputs. Its parameter list is broader overall, including logit_bias, logprobs, top_logprobs, repetition_penalty and seed, none of which Venice exposes. Venice covers the common sampling and formatting controls (frequency_penalty, max_tokens, presence_penalty, response_format, stop, temperature, top_k, top_p), which is enough for straightforward generation but not for agent style workflows. Neither model is flagged as a reasoning model in the comparison data, so there is no meaningful difference to report there.

For day to day work, pick based on what the request contains and what the response has to plug into. If you are sending plain text and want a general purpose writer or chat model, Venice is the natural fit, and it is the cheaper option on input tokens. If your input includes screenshots, diagrams, scanned pages or any other image, or if you need the model to call functions, return structured output, or carry a very long document in context, Qwen is the one to use. Qwen is also the cheaper side on output tokens, so long generations lean its way even for text only tasks. In short: text in, text out, simple parameters, Venice. Images, tools, structured output, or a very large context, Qwen.

Calling either model through api14 works the same way because the API is OpenAI compatible. You point your client at the api14 base URL, send a standard chat completions request, and set the model field to the id you want. To use Venice, set model to "cognitivecomputations/dolphin-mistral-24b-venice-edition". To use Qwen, set model to "qwen/qwen3-vl-8b-instruct". Everything else in the request body stays in the shape you already use, and switching between the two is a one line change of that model id. Just keep in mind that Qwen accepts image content parts in the messages array and Venice does not, and that tool and structured output fields only apply to Qwen.

There are still cases where Venice is the better pick even though Qwen looks more capable on paper. If your workload is pure text, you do not need tools or structured outputs, and your prompts fit comfortably inside 128000 tokens, Venice gives you the same basic chat completions interface with a smaller feature surface to reason about, and it is the cheaper model on input. Teams that want a plain text generator without multimodal or function calling machinery in the request path can stay on Venice and only move to Qwen when an image, a tool call, or a much longer context actually shows up.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "cognitivecomputations/dolphin-mistral-24b-venice-edition" to "qwen/qwen3-vl-8b-instruct" and prompts, streaming and tools keep working.

Venice: Uncensored details & playground →Qwen: Qwen3 VL 8B Instruct details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.chat.completions.create({
  "model": "qwen/qwen3-vl-8b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

More comparisons

Uncensored vs Qwen3 VL 8B Instruct: API price, context & features · api14