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Nemotron 3.5 Super VL vs Qwen3.8 Flash: API price, context & features · api14

NVIDIA: Nemotron 3.5 Super VL vs Qwen: Qwen3.8 Flash

A side-by-side comparison of NVIDIA: Nemotron 3.5 Super VL ($0.52 / 1M tokens) and Qwen: Qwen3.8 Flash ($0.52 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	NVIDIA: Nemotron 3.5 Super VL	Qwen: Qwen3.8 Flash
Provider	NVIDIA	Qwen
Input	$0.52 / 1M tokens	$0.52 / 1M tokens
Output	$1.75 / 1M tokens	$1.65 / 1M tokens
Cache read	$0.07 / 1M tokens	$0.06 / 1M tokens
Context window	1,048,576	1,000,000
Max output	943,718	131,072
Input	text, image, video	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 15, 2026	Aug 26, 2026

Key differences

Both have about the same input price.
Qwen: Qwen3.8 Flash is 6% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.00140 with NVIDIA: Nemotron 3.5 Super VL and $0.00135 with Qwen: Qwen3.8 Flash.

In depth

NVIDIA: Nemotron 3.5 Super VL is made by NVIDIA, and Qwen: Qwen3.8 Flash is made by Qwen. Both are text models in the sense that they return text, and both are offered through api14 as OpenAI-compatible endpoints. The two model ids you will pass in your requests are "nvidia/nemotron-3.5-super-vl" and "qwen/qwen3.8-flash". NVIDIA's model was released on 2026-10-15, while Qwen's model was released earlier, on 2026-08-26.

On capabilities the two are close. Both accept text, image and video as input and produce text only, so neither is a pure text-to-text model and neither generates images or audio. Their context windows are also nearly the same size: Nemotron 3.5 Super VL lists 1,048,576 tokens and Qwen3.8 Flash lists 1,000,000 tokens, a difference of roughly forty-eight thousand tokens, which is unlikely to decide a project on its own. The parameter lists overlap heavily, including tools, tool_choice, structured_outputs, response_format, reasoning, include_reasoning, temperature, top_p, top_k, seed, stop, logprobs and top_logprobs. Nemotron 3.5 Super VL additionally exposes logit_bias, min_p and repetition_penalty, which Qwen3.8 Flash does not list. On price, input cost is about the same for both, while output cost is lower on Qwen3.8 Flash.

For a developer choosing between them, the practical split follows from those details. If you need logit_bias to steer token probabilities, min_p as an alternative sampling cutoff, or repetition_penalty to fight loops in long generations, Nemotron 3.5 Super VL is the one that accepts those parameters, and Qwen3.8 Flash will reject or ignore them. If your workload is output heavy, for example long summaries, bulk generation or agent loops that emit a lot of tokens per call, Qwen3.8 Flash is the cheaper choice on the output side, and its input pricing is about the same as NVIDIA's, so the savings come from what the model writes rather than what you send. If your prompts are dominated by large video or image inputs with short answers, the input tie means cost is not a differentiator and you can decide on parameter support or on which model's behaviour you prefer in testing.

Calling either one through api14 works the same way because the API is OpenAI-compatible. You point your client at the api14 base URL, send your key, and set the model field to the id you want. Switching between the two is a one line change: replace "nvidia/nemotron-3.5-super-vl" with "qwen/qwen3.8-flash", or the reverse. Your messages array, image and video content parts, tool definitions, response_format and streaming setup all carry over unchanged, since both models accept the same core set of parameters. The only edits you should expect to make are removing parameters that only one side supports, such as logit_bias, min_p or repetition_penalty when you move to Qwen3.8 Flash.

There are cases where the other model is still the better pick. If you have already tuned prompts against Qwen3.8 Flash and your outputs are short, the output price advantage may not matter much, and Nemotron 3.5 Super VL's extra sampling controls could be worth more to you than the small saving. Conversely, if you never touch logit_bias, min_p or repetition_penalty, Qwen3.8 Flash gives you the same modalities, a context window within a few percent, and the same tool and structured output support at a lower output price, which makes it the simpler default.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "nvidia/nemotron-3.5-super-vl" to "qwen/qwen3.8-flash" and prompts, streaming and tools keep working.

NVIDIA: Nemotron 3.5 Super VL details & playground →Qwen: Qwen3.8 Flash 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/qwen3.8-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

NVIDIA: Nemotron 3.5 Super VL vs Qwen: Qwen3.8 Flash

Specifications

Key differences

In depth

Switch with one string

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