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DeepSeek V4.1 Flash vs Qwen3 VL 8B Instruct: API price, context & features · api14

DeepSeek: DeepSeek V4.1 Flash vs Qwen: Qwen3 VL 8B Instruct

A side-by-side comparison of DeepSeek: DeepSeek V4.1 Flash ($0.18 / 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

	DeepSeek: DeepSeek V4.1 Flash	Qwen: Qwen3 VL 8B Instruct
Provider	DeepSeek	Qwen
Input	$0.18 / 1M tokens	$0.88 / 1M tokens
Output	$1.98 / 1M tokens	$2.63 / 1M tokens
Cache read	$0.06 / 1M tokens	$0.42 / 1M tokens
Context window	1,048,576	262,144
Max output	943,718	235,929
Input	text, image	image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Sep 10, 2026	Oct 14, 2025

Key differences

DeepSeek: DeepSeek V4.1 Flash is 80% cheaper on input tokens.
DeepSeek: DeepSeek V4.1 Flash is 24% cheaper on output tokens.
DeepSeek: DeepSeek V4.1 Flash reads 4.0× more context (1,048,576 tokens).
Only DeepSeek: DeepSeek V4.1 Flash supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00117 with DeepSeek: DeepSeek V4.1 Flash and $0.00219 with Qwen: Qwen3 VL 8B Instruct.

In depth

DeepSeek: DeepSeek V4.1 Flash is a text-focused model from DeepSeek, released on 2026-09-10. Qwen: Qwen3 VL 8B Instruct comes from Qwen and was released earlier, on 2025-10-14. Both are listed under the text category on api14, and both accept text and image input while returning text output, so neither is limited to plain text prompts.

The clearest split is context and reasoning support. DeepSeek V4.1 Flash offers a context window of 1048576 tokens, while Qwen3 VL 8B Instruct offers 262144 tokens, so DeepSeek gives you roughly four times the room for long documents, large codebases or extended conversations. DeepSeek is also the only one of the two that exposes reasoning controls, including reasoning, reasoning_effort and include_reasoning, along with extra sampling knobs such as min_p, top_a and parallel_tool_calls. Qwen3 VL 8B Instruct supports a narrower parameter set, though it still covers the essentials: temperature, top_p, top_k, tools, tool_choice, structured_outputs, response_format, seed, stop and the penalty parameters. Both support tool calling and structured outputs, so that is not a deciding factor. On price, DeepSeek is the cheaper option for both input and output.

For most day to day work, the choice comes down to how much context and control you need. If you are feeding in long PDFs, multi-file repositories, lengthy transcripts or anything that would otherwise need chunking, DeepSeek V4.1 Flash is the natural pick because of its much larger window. If you want explicit reasoning effort settings, or you are tuning sampling with min_p and top_a, DeepSeek is also the one that exposes those. Qwen3 VL 8B Instruct is a reasonable fit for smaller, well scoped tasks: single images with a question, short documents, classification, extraction or straightforward tool calling where the prompt fits comfortably inside 262144 tokens. It is an 8B class model, so expect it to be lighter, and it handles image plus text input just like DeepSeek does.

Calling either model through api14 works the same way, because the gateway is OpenAI-compatible. You point your client at the api14 base URL, send your request to the chat completions endpoint with your api14 key, and set the model field to the id you want. Switching between the two is a one line change: use deepseek/deepseek-v4.1-flash for DeepSeek and qwen/qwen3-vl-8b-instruct for Qwen. The message format, image content parts, tool definitions and response parsing stay identical, so you can build against one and swap the id to compare outputs. Just remember that parameters outside a model's supported list, such as reasoning_effort on Qwen, will not apply.

There is still a case for Qwen3 VL 8B Instruct even though DeepSeek is cheaper and has the larger window. If your workload is small and image-centric, for example captioning, visual question answering or extracting fields from a screenshot, the 8B model is more than adequate and keeps your requests simple. It is also a sensible fallback if you want a second opinion from a different provider on the same prompt, since you can run both ids side by side and compare the text output without changing any other part of your code.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "deepseek/deepseek-v4.1-flash" to "qwen/qwen3-vl-8b-instruct" and prompts, streaming and tools keep working.

DeepSeek: DeepSeek V4.1 Flash 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);

DeepSeek: DeepSeek V4.1 Flash vs Qwen: Qwen3 VL 8B Instruct

Specifications

Key differences

In depth

Switch with one string

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