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Devstral 2 2512 vs Qwen3.5-27B: API price, context & features · api14

Mistral: Devstral 2 2512 vs Qwen: Qwen3.5-27B

A side-by-side comparison of Mistral: Devstral 2 2512 ($1.40 / 1M tokens) and Qwen: Qwen3.5-27B ($0.91 / 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	Qwen: Qwen3.5-27B
Provider	Mistral	Qwen
Input	$1.40 / 1M tokens	$0.91 / 1M tokens
Output	$7.00 / 1M tokens	$9.10 / 1M tokens
Cache read	$0.14 / 1M tokens	-
Context window	262,144	262,144
Max output	209,715	81,920
Input	text, file	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Dec 9, 2025	Feb 25, 2026

Key differences

Qwen: Qwen3.5-27B is 35% cheaper on input tokens.
Mistral: Devstral 2 2512 is 23% cheaper on output tokens.
Only Qwen: Qwen3.5-27B supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00490 with Mistral: Devstral 2 2512 and $0.00546 with Qwen: Qwen3.5-27B.

In depth

Mistral: Devstral 2 2512 is made by Mistral, and Qwen: Qwen3.5-27B is made by Qwen. Both are text generation models, meaning both take input and return text, and both are served through api14 under the same OpenAI-compatible interface. Devstral 2 2512 was released by its provider on 2025-12-09, while Qwen3.5-27B carries a provider release date of 2026-02-25, so the Qwen model is the newer of the two.

The two models share a context window of 262144 tokens, so neither has an advantage there. Where they diverge is in input modalities and tuning options. Devstral 2 2512 accepts text and file input, while Qwen3.5-27B accepts text, image and video input, which makes the Qwen model the one to reach for when your prompt includes visual media. Qwen3.5-27B also exposes reasoning controls (include_reasoning and reasoning) that Devstral 2 2512 does not list, along with logit_bias, logprobs, top_logprobs, min_p, top_k and repetition_penalty. Devstral 2 2512 lists a smaller parameter set, but both models support tools, tool_choice, structured_outputs, response_format, temperature, top_p, seed, stop, max_tokens, frequency_penalty and presence_penalty, so function calling and structured output work on either side.

For a developer choosing between them, the decision usually comes down to input type and cost shape rather than raw context. If your task is code or text work over large files, Devstral 2 2512 handles file input directly and is the cheaper option on output tokens, which matters when the model writes long responses. If your task involves images or video, or you want reasoning traces and finer sampling controls such as top_k, min_p or logprobs, Qwen3.5-27B is the fit, and it is the cheaper option on input tokens, which matters when you send large prompts. When both could work, weigh whether your traffic is input-heavy or output-heavy, since the two models are cheaper on opposite sides of that split.

Calling either model through api14 works the same way, because the gateway is OpenAI-compatible. You point your client at the api14 base URL, send your usual chat completions request, and set the model field to "mistralai/devstral-2512" or "qwen/qwen3.5-27b". Everything else in the request body, including messages, tools and response_format, stays as it is. Switching between the two is a one-line change to the model id, so you can benchmark both against your own workload without rewriting integration code.

There are cases where the model you did not pick is still the better choice. If you need reasoning output or image and video input, Devstral 2 2512 cannot serve that request, so Qwen3.5-27B wins regardless of price. If you need file input and your workload is dominated by generated output, Qwen3.5-27B is the weaker pick on cost, and Devstral 2 2512 is the sensible default. Check the live price table on this page for current rates before you commit, since the cheaper side depends on your input-to-output ratio.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "mistralai/devstral-2512" to "qwen/qwen3.5-27b" and prompts, streaming and tools keep working.

Mistral: Devstral 2 2512 details & playground →Qwen: Qwen3.5-27B 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/qwen3.5-27b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Mistral: Devstral 2 2512 vs Qwen: Qwen3.5-27B

Specifications

Key differences

In depth

Switch with one string

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