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Nemotron 3.5 Super VL vs GLM 5.3 Flash: API price, context & features · api14

NVIDIA: Nemotron 3.5 Super VL vs Z.ai: GLM 5.3 Flash

A side-by-side comparison of NVIDIA: Nemotron 3.5 Super VL ($0.52 / 1M tokens) and Z.ai: GLM 5.3 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	Z.ai: GLM 5.3 Flash
Provider	NVIDIA	Z.ai
Input	$0.52 / 1M tokens	$0.52 / 1M tokens
Output	$1.75 / 1M tokens	$1.75 / 1M tokens
Cache read	$0.07 / 1M tokens	$0.11 / 1M tokens
Context window	1,048,576	1,048,576
Max output	943,718	943,717
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.
A request with 1,000 input and 500 output tokens costs $0.00140 with NVIDIA: Nemotron 3.5 Super VL and $0.00140 with Z.ai: GLM 5.3 Flash.

In depth

NVIDIA: Nemotron 3.5 Super VL is a model from NVIDIA, and Z.ai: GLM 5.3 Flash is a model from Z.ai. Both are text generation models that accept text, image and video as input and return text as output, and both are served through api14 alongside each other. They are close siblings in the sense that they occupy the same slot in a developer's stack: a large multimodal model you call when you need one endpoint to handle mixed media prompts and long documents.

The two models are remarkably similar on paper. Both expose a context window of 1,048,576 tokens, so neither has an advantage on how much you can feed in at once. Both accept the same three input modalities and produce text only. The parameter lists overlap almost completely, covering sampling controls such as temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty and repetition_penalty, plus seed, stop, logit_bias, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, reasoning and include_reasoning. The differences are narrow: GLM 5.3 Flash adds parallel_tool_calls and reasoning_effort, while Nemotron 3.5 Super VL does not list those two. On pricing, the qualitative comparison puts input and output cost at a tie, so neither is meaningfully cheaper than the other. The release dates differ, with GLM 5.3 Flash arriving on 2026-08-26 and Nemotron 3.5 Super VL on 2026-10-15.

For most tasks the choice will come down to small ergonomic details rather than raw capability. If your workflow involves issuing several tool calls in one turn and you want the model to run them in parallel, GLM 5.3 Flash exposes parallel_tool_calls, which Nemotron 3.5 Super VL does not list. If you want to dial reasoning effort up or down per request, reasoning_effort is likewise only on the Z.ai model. Nemotron 3.5 Super VL, meanwhile, covers the same core surface for structured outputs, tool calling and reasoning, so any task built around a single tool call, a JSON schema response, or a straightforward reasoning pass should behave comparably on either. Given the tied pricing and identical context length, there is no cost or capacity argument pushing you one way.

Switching between them is trivial because api14 exposes an OpenAI-compatible API. You point your client at the same base URL, send the same request body, and change only the model field between "nvidia/nemotron-3.5-super-vl" and "z-ai/glm-5.3-flash". Your messages array, image or video content parts, tool definitions, response_format and sampling parameters all carry over unchanged, with the caveat that parallel_tool_calls and reasoning_effort will only take effect on the Z.ai model. That makes it practical to keep both configured and route per request, or to A/B them on your own traffic without rewriting integration code.

A reasonable default is to start with GLM 5.3 Flash if your application leans on multi tool orchestration or tunable reasoning effort, since those are the only listed capabilities where it goes beyond the NVIDIA model. Reach for Nemotron 3.5 Super VL when you want the same long context multimodal behaviour from NVIDIA and do not need those two extra controls. Because pricing is tied and the context window is identical, the deciding factors are the two extra parameters and whichever provider you already prefer, not any difference in cost or capacity.

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 "z-ai/glm-5.3-flash" and prompts, streaming and tools keep working.

NVIDIA: Nemotron 3.5 Super VL details & playground →Z.ai: GLM 5.3 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": "z-ai/glm-5.3-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 Z.ai: GLM 5.3 Flash

Specifications

Key differences

In depth

Switch with one string

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