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Gemini 3.1 Flash Lite Preview vs Qwen2.5 Coder 32B Instruct: API price, context & features · api14

Google: Gemini 3.1 Flash Lite Preview vs Qwen2.5 Coder 32B Instruct

A side-by-side comparison of Google: Gemini 3.1 Flash Lite Preview ($0.88 / 1M tokens) and Qwen2.5 Coder 32B Instruct ($2.31 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Gemini 3.1 Flash Lite Preview	Qwen2.5 Coder 32B Instruct
Provider	Google	Qwen
Input	$0.88 / 1M tokens	$2.31 / 1M tokens
Output	$5.25 / 1M tokens	$3.50 / 1M tokens
Cache read	$0.09 / 1M tokens	-
Cache write	$0.29 / 1M tokens	-
Reasoning	$5.25 / 1M tokens	-
Audio input	$1.75 / 1M tokens	-
Image	$0.000001 / image	-
Web search	$49.00 / 1K searches	-
Context window	1,048,576	32,768
Max output	65,536	29,491
Input	text, image, video, file, audio	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Mar 3, 2026	Nov 11, 2024

Key differences

Google: Gemini 3.1 Flash Lite Preview is 62% cheaper on input tokens.
Qwen2.5 Coder 32B Instruct is 33% cheaper on output tokens.
Google: Gemini 3.1 Flash Lite Preview reads 32.0× more context (1,048,576 tokens).
Only Google: Gemini 3.1 Flash Lite Preview supports tool calling.
Only Google: Gemini 3.1 Flash Lite Preview supports structured JSON output.
Only Google: Gemini 3.1 Flash Lite Preview supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00350 with Google: Gemini 3.1 Flash Lite Preview and $0.00406 with Qwen2.5 Coder 32B Instruct.

In depth

Google: Gemini 3.1 Flash Lite Preview is a text model from Google, released as a preview on 2026-03-03. Qwen2.5 Coder 32B Instruct is a text model from Qwen, released on 2024-11-11. Both are served on api14 under the text category, so at the surface level they occupy the same slot in a request: you send a prompt, you get text back. The differences show up in what each model accepts, how much it can hold at once, and which request parameters it honors.

The clearest split is context and modality. Gemini 3.1 Flash Lite Preview has a context window of 1,048,576 tokens, while Qwen2.5 Coder 32B Instruct has 32,768 tokens, so Gemini holds far more in a single call. Gemini also accepts text, image, video, file and audio input, whereas Qwen2.5 Coder 32B Instruct accepts text only. On the parameter side, Gemini supports tools, tool_choice, structured_outputs, response_format, reasoning, reasoning_effort and include_reasoning, and Qwen2.5 Coder 32B Instruct does not. Qwen2.5 Coder 32B Instruct instead exposes frequency_penalty, logit_bias, min_p, presence_penalty, repetition_penalty, stop and top_k, which Gemini does not list. Both accept max_tokens, temperature, top_p, seed and response_format.

For task selection, the deciding factors are input type, context size and whether you need tool calls or structured output. If your request includes an image, a video, a file or audio, or if the prompt plus retrieved context runs past roughly 32k tokens, Gemini 3.1 Flash Lite Preview is the one that can take it. The same applies if you want the model to call tools, emit structured outputs, or expose reasoning effort. Qwen2.5 Coder 32B Instruct fits text-only work that stays inside 32k tokens, and its extra sampling controls (frequency_penalty, presence_penalty, repetition_penalty, min_p, top_k, logit_bias, stop) give you finer control over repetition and stopping behavior when you are tuning output. On price, Gemini is the cheaper input side and Qwen is the cheaper output side, so the cost balance shifts with 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 a chat completions request with your messages, and set the model field to "google/gemini-3.1-flash-lite-preview" or "qwen/qwen-2.5-coder-32b-instruct". Nothing else in the request shape has to change for a basic text call. If you switch to Gemini, you can add tools, tool_choice, structured_outputs, reasoning or reasoning_effort; if you switch to Qwen, you can add frequency_penalty, presence_penalty, repetition_penalty, min_p, top_k, logit_bias or stop. Keep the parameter set matched to the model you are calling, since each model only accepts the parameters listed for it.

There are still cases where Qwen2.5 Coder 32B Instruct is the better pick. If your workload is text-only, fits within 32k tokens, and you specifically want the sampling controls it exposes, such as repetition_penalty or top_k, then it gives you knobs Gemini does not offer. It is also the cheaper output side, so for generation-heavy text tasks where output tokens dominate, it can be the more economical choice.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-3.1-flash-lite-preview" to "qwen/qwen-2.5-coder-32b-instruct" and prompts, streaming and tools keep working.

Google: Gemini 3.1 Flash Lite Preview details & playground →Qwen2.5 Coder 32B Instruct 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/qwen-2.5-coder-32b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemini 3.1 Flash Lite Preview vs Qwen2.5 Coder 32B Instruct

Specifications

Key differences

In depth

Switch with one string

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