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OpenAI: GPT-4.1 Nano vs Qwen: Qwen3.5-9B

A side-by-side comparison of OpenAI: GPT-4.1 Nano ($0.35 / 1M tokens) and Qwen: Qwen3.5-9B ($0.60 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	OpenAI: GPT-4.1 Nano	Qwen: Qwen3.5-9B
Provider	OpenAI	Qwen
Input	$0.35 / 1M tokens	$0.60 / 1M tokens
Output	$1.40 / 1M tokens	$0.88 / 1M tokens
Cache read	$0.11 / 1M tokens	-
Web search	$35.00 / 1K searches	-
Context window	1,047,576	262,144
Max output	942,818	235,929
Input	image, text, file	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 14, 2025	Mar 10, 2026

Key differences

OpenAI: GPT-4.1 Nano is 41% cheaper on input tokens.
Qwen: Qwen3.5-9B is 38% cheaper on output tokens.
OpenAI: GPT-4.1 Nano reads 4.0× more context (1,047,576 tokens).
Only Qwen: Qwen3.5-9B supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00105 with OpenAI: GPT-4.1 Nano and $0.00103 with Qwen: Qwen3.5-9B.

In depth

OpenAI: GPT-4.1 Nano is made by OpenAI, and Qwen: Qwen3.5-9B is made by Qwen. Both are text models that take input and return text, and both are served through api14 under the same OpenAI-compatible interface. The practical difference starts with who built them and what each was tuned for: one comes from OpenAI's GPT-4.1 line, the other from the Qwen3.5 family.

The clearest split is context and modalities. GPT-4.1 Nano has a much larger context window, over a million tokens, while Qwen3.5-9B sits at 262144 tokens. That matters if you are feeding long documents, large codebases or long transcripts in a single request. On input types, GPT-4.1 Nano accepts image, text and file input, whereas Qwen3.5-9B accepts text, image and video. So if your pipeline includes video, Qwen is the one that can see it; if it includes arbitrary files, GPT-4.1 Nano is the one that can take them. Only Qwen3.5-9B exposes reasoning controls, so tasks that benefit from explicit reasoning parameters point to Qwen. Both support tools and structured outputs, so neither has an edge there.

For day to day work, pick based on the shape of the request rather than a general winner. If you are summarizing or querying very long text, or you need to pass files alongside the prompt, GPT-4.1 Nano is the natural choice because of its larger context and file input. If you are working with video, or you want reasoning-related parameters and a wider set of sampling controls such as top_k, min_p, repetition_penalty and logprobs, Qwen3.5-9B fits better. Qwen is also the cheaper option on output, while GPT-4.1 Nano is cheaper on input, so the cost balance depends on whether your workload is input heavy or output heavy. Check the live price table for the actual numbers.

Calling either model through api14 works the same way. You send a request to the OpenAI-compatible endpoint and change only the model id: use "openai/gpt-4.1-nano" for GPT-4.1 Nano and "qwen/qwen3.5-9b" for Qwen3.5-9B. The request and response shapes stay the same, so you can switch models by swapping that string, and you can keep both ids in configuration if you want to route different jobs to different models. Parameters that only one model supports, such as the reasoning controls on Qwen, should be sent only when you are calling that model.

There are still cases where the other model wins. If your task is output heavy and cost sensitive, Qwen3.5-9B is the cheaper side on output, even though GPT-4.1 Nano is cheaper on input. And if you need reasoning parameters, GPT-4.1 Nano does not expose them, so Qwen3.5-9B remains the better pick regardless of context size.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "openai/gpt-4.1-nano" to "qwen/qwen3.5-9b" and prompts, streaming and tools keep working.

OpenAI: GPT-4.1 Nano details & playground →Qwen: Qwen3.5-9B 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/qwen3.5-9b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

More comparisons

GPT-4.1 Nano vs Qwen3.5-9B: API price, context & features · api14