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Cohere: Command A+ vs Qwen: Qwen3.5 Plus 2026-02-15

A side-by-side comparison of Cohere: Command A+ ($1.05 / 1M tokens) and Qwen: Qwen3.5 Plus 2026-02-15 ($0.91 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Cohere: Command A+	Qwen: Qwen3.5 Plus 2026-02-15
Provider	Cohere	Qwen
Input	$1.05 / 1M tokens	$0.91 / 1M tokens
Output	$5.25 / 1M tokens	$5.46 / 1M tokens
Cache read	$0.52 / 1M tokens	-
Context window	192,000	1,000,000
Max output	64,000	65,536
Input	text, image	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Sep 22, 2026	Feb 16, 2026

Key differences

Qwen: Qwen3.5 Plus 2026-02-15 is 13% cheaper on input tokens.
Qwen: Qwen3.5 Plus 2026-02-15 reads 5.2× more context (1,000,000 tokens).
A request with 1,000 input and 500 output tokens costs $0.00367 with Cohere: Command A+ and $0.00364 with Qwen: Qwen3.5 Plus 2026-02-15.

In depth

Cohere: Command A+ is a text generation model from Cohere, released on 2026-09-22. Qwen: Qwen3.5 Plus 2026-02-15 comes from Qwen and was released on 2026-02-16. Both are listed on api14 under the text category, and both take text as their primary input and return text output. If you are already choosing between vendors for a general purpose text workload, these two sit in the same slot in the catalogue, so the decision comes down to context size, input modalities and the parameter surface each one exposes.

The clearest split is context. Qwen3.5 Plus carries a context window of 1,000,000 tokens, while Command A+ sits at 192,000 tokens, so Qwen is the larger of the two by a wide margin. On input modalities, Command A+ accepts text and images, whereas Qwen3.5 Plus accepts text, images and video, so Qwen covers one more input type. Both return text only. The parameter lists overlap heavily: both support frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tools, top_k and top_p. Qwen adds logprobs, tool_choice and top_logprobs, which matters if you want token level probabilities or explicit control over whether a tool is called. On price, Qwen is the cheaper option for input, while output pricing is about the same for both, so cost differences will show up mainly on the input side.

For day to day work, the choice usually follows the input. If your task is text only, or text plus a handful of images, and your prompts fit comfortably inside 192,000 tokens, Command A+ is a reasonable default and its parameter set is broad enough for most structured output and tool calling patterns. If you are feeding in long documents, whole codebases, lengthy transcripts, or video, Qwen3.5 Plus is the better fit because of the larger context window and the extra video modality. The logprobs and top_logprobs parameters on Qwen also make it the pick when you need to inspect model confidence or build scoring on top of the raw output, and tool_choice gives you finer control over function calling behaviour.

Calling either model through api14 works the same way, because the gateway exposes an OpenAI compatible API. You point your client at the api14 base URL, send a chat completions request with your messages, and set the model field to either "cohere/command-a-plus" or "qwen/qwen3.5-plus-02-15". Everything else in the request body stays the same shape, so switching between the two is a one line change to the model id. Parameters that both models support, such as temperature, max_tokens, tools and response_format, can be passed without modification. If you use logprobs, top_logprobs or tool_choice, those are only meaningful on the Qwen id, so guard them in your code or strip them when you fall back to Command A+.

There are still cases where Command A+ is the better pick even though Qwen has the larger context and the extra modality. Its 192,000 token window is generous for most chat, extraction and summarisation jobs, and it accepts images, so a vision task that does not involve video is fully covered. If you have already tuned prompts against Cohere's behaviour, or you want to keep a mixed vendor setup so a single provider outage does not take down your whole pipeline, keeping Command A+ in the rotation makes sense. The two models are close enough on output price that running both and routing per request is practical rather than costly.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "cohere/command-a-plus" to "qwen/qwen3.5-plus-02-15" and prompts, streaming and tools keep working.

Cohere: Command A+ details & playground →Qwen: Qwen3.5 Plus 2026-02-15 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-plus-02-15",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

More comparisons

Command A+ vs Qwen3.5 Plus 2026-02-15: API price, context & features · api14