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Qwen3.5-27B vs GLM 4.5V: API price, context & features · api14

Qwen: Qwen3.5-27B vs Z.ai: GLM 4.5V

A side-by-side comparison of Qwen: Qwen3.5-27B ($0.91 / 1M tokens) and Z.ai: GLM 4.5V ($2.10 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Qwen: Qwen3.5-27B	Z.ai: GLM 4.5V
Provider	Qwen	Z.ai
Input	$0.91 / 1M tokens	$2.10 / 1M tokens
Output	$9.10 / 1M tokens	$6.30 / 1M tokens
Cache read	-	$0.39 / 1M tokens
Context window	262,144	65,536
Max output	81,920	16,384
Input	text, image, video	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Feb 25, 2026	Aug 11, 2025

Key differences

Qwen: Qwen3.5-27B is 57% cheaper on input tokens.
Z.ai: GLM 4.5V is 31% cheaper on output tokens.
Qwen: Qwen3.5-27B reads 4.0× more context (262,144 tokens).
Only Qwen: Qwen3.5-27B supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00546 with Qwen: Qwen3.5-27B and $0.00525 with Z.ai: GLM 4.5V.

In depth

Qwen: Qwen3.5-27B is made by Qwen and carries the model id qwen/qwen3.5-27b. Z.ai: GLM 4.5V is made by Z.ai and carries the model id z-ai/glm-4.5v. Both are listed in the text category, and both accept text input and return text output, so at the level of basic shape they are the same kind of model. The differences show up in how much they accept and how they are tuned.

The clearest split is context length. Qwen3.5-27B offers 262144 tokens, while GLM 4.5V offers 65536, so Qwen gives you roughly four times the room before you need to trim or chunk. Qwen also accepts video as an input modality alongside text and image, while GLM 4.5V accepts text and image only. On parameters, Qwen supports structured outputs and a wider set that includes logit_bias, logprobs, min_p, top_logprobs and structured_outputs, while GLM 4.5V does not list those. Both support tools, tool_choice, reasoning and include_reasoning, so tool calling and reasoning are not a differentiator here. On price, Qwen is the cheaper side for input and GLM 4.5V is the cheaper side for output, so the cost picture depends on your input to output ratio.

For practical selection, reach for Qwen3.5-27B when your prompt is long, when you need to pass video, or when you want structured outputs, logprobs or the finer sampling controls. Long documents, multi hour transcripts, and pipelines that need a strict response schema all fit it well. Reach for GLM 4.5V when your inputs are short to moderate, when you are text and image only, and when output volume dominates your bill, since it is the cheaper side on output. If your workload is mostly short prompts with long generations, GLM 4.5V can be the more economical call even though its context window is smaller.

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 your key, and use the chat completions shape you already use elsewhere. The only change between the two is the model id: set it to qwen/qwen3.5-27b or z-ai/glm-4.5v. Everything else in the request body stays as it is, and you can keep one code path and switch the id per request or per environment. Check the live price table on this page for current rates rather than hardcoding assumptions.

There are still cases where the smaller context model is the better pick. If your task is short image and text questions with long answers, GLM 4.5V's cheaper output side matters more than Qwen's larger window, and you will not use the extra context anyway. Likewise, if you do not need structured outputs, logprobs or video, the extra parameters on Qwen buy you nothing, and the simpler choice is fine. Match the model to the shape of your traffic rather than to the longer feature list.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "qwen/qwen3.5-27b" to "z-ai/glm-4.5v" and prompts, streaming and tools keep working.

Qwen: Qwen3.5-27B details & playground →Z.ai: GLM 4.5V 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-4.5v",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Qwen: Qwen3.5-27B vs Z.ai: GLM 4.5V

Specifications

Key differences

In depth

Switch with one string

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