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Devstral 2 2512 vs GLM 4.5V: API price, context & features · api14

Mistral: Devstral 2 2512 vs Z.ai: GLM 4.5V

A side-by-side comparison of Mistral: Devstral 2 2512 ($1.40 / 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

	Mistral: Devstral 2 2512	Z.ai: GLM 4.5V
Provider	Mistral	Z.ai
Input	$1.40 / 1M tokens	$2.10 / 1M tokens
Output	$7.00 / 1M tokens	$6.30 / 1M tokens
Cache read	$0.14 / 1M tokens	$0.39 / 1M tokens
Context window	262,144	65,536
Max output	209,715	16,384
Input	text, file	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Dec 9, 2025	Aug 11, 2025

Key differences

Mistral: Devstral 2 2512 is 33% cheaper on input tokens.
Z.ai: GLM 4.5V is 10% cheaper on output tokens.
Mistral: Devstral 2 2512 reads 4.0× more context (262,144 tokens).
Only Mistral: Devstral 2 2512 supports structured JSON output.
Only Z.ai: GLM 4.5V supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00490 with Mistral: Devstral 2 2512 and $0.00525 with Z.ai: GLM 4.5V.

In depth

Mistral: Devstral 2 2512 is made by Mistral, and Z.ai: GLM 4.5V is made by Z.ai. Both are listed on api14 as text models, meaning the final answer you get back from either one is text. Devstral 2 2512 was released by its provider on 2025-12-09, while GLM 4.5V was released on 2025-08-11. They come from different vendors with different design priorities, so the choice between them is mostly about the shape of your input and how much context you need to send.

The clearest split is in input modalities and context length. Devstral 2 2512 accepts text and files, and it carries a context window of 262144 tokens. GLM 4.5V accepts text and images, with a context window of 65536 tokens. So Devstral 2 2512 gives you the bigger context by a wide margin, which matters when you are feeding in long documents, large files or extended conversation history. GLM 4.5V is the one to reach for when your input includes images, since Devstral 2 2512 does not list image input. On parameters, Devstral 2 2512 supports structured outputs, which GLM 4.5V does not list, while GLM 4.5V supports reasoning and include_reasoning, which Devstral 2 2512 does not list. Both support tools and tool_choice, so function calling is available on either side. GLM 4.5V also exposes top_k and repetition_penalty, which Devstral 2 2512 does not list.

For practical work, pick Devstral 2 2512 when the task is text or file heavy and the prompt is long: large code files, multi file repositories, long specifications, or anything where you want the model to emit a strict JSON shape through structured outputs. Pick GLM 4.5V when the task involves an image alongside text, such as reading a screenshot, a diagram or a scanned page, or when you want the model to expose its reasoning steps. If your workload is plain text with no images and fits comfortably inside 65536 tokens, both are viable, and the deciding factors become price and whether you need structured outputs or reasoning. On price, Devstral 2 2512 is the cheaper of the two for input, while GLM 4.5V is cheaper for output, so the better deal depends on your input to output ratio. The live price table on this page has the current numbers.

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 request to the chat completions endpoint with your api14 key, and set the model field to the id you want. To use Mistral's model, set the model to "mistralai/devstral-2512". To use Z.ai's model, set it to "z-ai/glm-4.5v". Everything else in the request body stays the same: messages, temperature, max_tokens and the rest. Switching between the two is a one line change to the model id, so you can compare them on your own traffic without rewriting your integration.

There are cases where the other model is still the better pick even after you have chosen. If you start on Devstral 2 2512 for its long context but your prompt turns out to include an image, GLM 4.5V is the one that accepts it. If you start on GLM 4.5V for image input but your document grows past 65536 tokens, you will need Devstral 2 2512 for the larger window. And if you need structured outputs, that points to Devstral 2 2512, while a need for visible reasoning points to GLM 4.5V.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "mistralai/devstral-2512" to "z-ai/glm-4.5v" and prompts, streaming and tools keep working.

Mistral: Devstral 2 2512 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);

Mistral: Devstral 2 2512 vs Z.ai: GLM 4.5V

Specifications

Key differences

In depth

Switch with one string

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