Qwen: Qwen3.6 27B vs Z.ai: GLM 4.5V
A side-by-side comparison of Qwen: Qwen3.6 27B ($1.58 / 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: Qwen3.6 27B | Z.ai: GLM 4.5V | |
|---|---|---|
| Provider | Qwen | Z.ai |
| Input | $1.58 / 1M tokens | $2.10 / 1M tokens |
| Output | $9.45 / 1M tokens | $6.30 / 1M tokens |
| Cache read | - | $0.39 / 1M tokens |
| Context window | 262,144 | 65,536 |
| Max output | 65,536 | 16,384 |
| Input | text, image, video | text, image |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Apr 27, 2026 | Aug 11, 2025 |
Key differences
- Qwen: Qwen3.6 27B is 25% cheaper on input tokens.
- Z.ai: GLM 4.5V is 33% cheaper on output tokens.
- Qwen: Qwen3.6 27B reads 4.0× more context (262,144 tokens).
- Only Qwen: Qwen3.6 27B supports structured JSON output.
- A request with 1,000 input and 500 output tokens costs $0.00630 with Qwen: Qwen3.6 27B and $0.00525 with Z.ai: GLM 4.5V.
In depth
Qwen: Qwen3.6 27B is a text model from Qwen, released on 2026-04-27. Z.ai: GLM 4.5V is a text model from Z.ai, released on 2025-08-11. Both are served through api14 as chat models with text output, so the choice between them comes down to context size, what you can send in, and which sampling and output controls you need.
The clearest split is context length. Qwen3.6 27B accepts up to 262144 tokens, while GLM 4.5V accepts up to 65536, so Qwen gives you roughly four times the room for long documents, large codebases or extended conversation history. On input, Qwen3.6 27B takes text, images and video, whereas GLM 4.5V takes text and images only, so video input is a Qwen-only capability here. Both output text only. Qwen3.6 27B also exposes a wider parameter surface: it supports structured_outputs, logit_bias, logprobs, top_logprobs and min_p, none of which appear on GLM 4.5V. Both models support reasoning and include_reasoning, so neither has an edge there. On price, Qwen3.6 27B is the cheaper of the two for input, while GLM 4.5V is cheaper for output, so the better value depends on whether your workload is input-heavy or output-heavy.
For practical work, reach for Qwen3.6 27B when the task involves very long inputs, video alongside text, or when you need structured outputs, logprobs or fine-grained control over token selection through logit_bias and min_p. Long-context retrieval, whole-repository review, and pipelines that parse model output programmatically all fit it well. GLM 4.5V is a reasonable pick when your inputs fit comfortably inside its context window, you only need text and images, and your workload generates a lot of output tokens, since its output pricing is the lower of the two. It still supports tools, tool_choice, response_format, reasoning and the usual sampling parameters, so ordinary tool-calling and JSON-shaped responses remain available.
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 request to the chat completions endpoint, and set the model field to either "qwen/qwen3.6-27b" or "z-ai/glm-4.5v". Everything else in the request body, messages, temperature, max_tokens, tools, stays as you would write it for OpenAI. Switching between the two is a one-line change to the model id, so you can route different requests to different models without rewriting your integration.
There are cases where the model that looks weaker on paper is still the better call. If your task is image and text only, fits inside 65536 tokens, and produces long answers, GLM 4.5V can be the more economical choice because its output pricing is lower. Conversely, if you need video input, a very large context window, or structured outputs and logprobs, Qwen3.6 27B is the one to use, since those capabilities are not available on GLM 4.5V.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "qwen/qwen3.6-27b" to "z-ai/glm-4.5v" and prompts, streaming and tools keep working.
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);
More comparisons
- LiquidAI: LFM3-1.7B vs Ricursive: Ripple 65B Preview
- LiquidAI: LFM3-1.7B vs Crouton Alpha
- LiquidAI: LFM3-1.7B vs Tencent: Hy4
- LiquidAI: LFM3-1.7B vs Z.ai: GLM 5.3 FlashX Legal
- Ricursive: Ripple 65B Preview vs Crouton Alpha
- Ricursive: Ripple 65B Preview vs Tencent: Hy4
- Ricursive: Ripple 65B Preview vs Z.ai: GLM 5.3 FlashX Legal
- Crouton Alpha vs Tencent: Hy4