Google: Gemini 3.1 Flash Lite Preview vs Qwen: Qwen3.5 Plus 2026-02-15
A side-by-side comparison of Google: Gemini 3.1 Flash Lite Preview ($0.88 / 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
| Google: Gemini 3.1 Flash Lite Preview | Qwen: Qwen3.5 Plus 2026-02-15 | |
|---|---|---|
| Provider | Qwen | |
| Input | $0.88 / 1M tokens | $0.91 / 1M tokens |
| Output | $5.25 / 1M tokens | $5.46 / 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 | 1,000,000 |
| Max output | 65,536 | 65,536 |
| Input | text, image, video, file, audio | text, image, video |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Mar 3, 2026 | Feb 16, 2026 |
Key differences
- Both have about the same input price.
- A request with 1,000 input and 500 output tokens costs $0.00350 with Google: Gemini 3.1 Flash Lite Preview and $0.00364 with Qwen: Qwen3.5 Plus 2026-02-15.
In depth
Google: Gemini 3.1 Flash Lite Preview is a text model from Google, listed under the model id google/gemini-3.1-flash-lite-preview. Qwen: Qwen3.5 Plus 2026-02-15 is a text model from Qwen, listed under the model id qwen/qwen3.5-plus-02-15. Both are served through api14 as chat style models with text output, so from the caller's point of view they occupy the same slot in your code: you send a conversation in and you get text back. The provider release dates are close together, 2026-03-03 for the Gemini model and 2026-02-16 for the Qwen model, which puts both in the same recent generation of hosted models.
The clearest difference is in what you can feed them. Gemini 3.1 Flash Lite Preview accepts text, image, video, file and audio input, while Qwen3.5 Plus accepts text, image and video. If your pipeline needs to hand over an audio clip or an uploaded document as a file part, the Gemini model covers that and the Qwen model does not. Context length is close: 1,048,576 tokens for Gemini and 1,000,000 tokens for Qwen, so both can hold very large inputs and neither is meaningfully bigger for practical purposes. On parameters the two overlap on the core set you would expect, including temperature, top_p, max_tokens, seed, response_format, structured_outputs, tool_choice, tools and reasoning controls. Qwen adds frequency_penalty, presence_penalty, stop, top_k, logprobs and top_logprobs, which matters if you rely on token level probabilities or on sampling controls beyond the basics. The qualitative price fields mark input and output cost as a tie, so neither is the cheaper option in any way you should plan around.
For task selection, start from the input side. If you are building something that ingests audio, or that treats arbitrary uploaded files as first class input alongside text and images, Gemini 3.1 Flash Lite Preview is the natural pick because it is the only one of the two that lists those modalities. If your inputs are text, images and video only, both are viable, and the decision shifts to sampling and introspection: Qwen3.5 Plus gives you penalties, stop sequences, top_k and logprob outputs, which are useful when you are tuning generation behaviour or debugging why a particular token was chosen. If you need reasoning controls, both expose them, so that is not a tiebreaker. For straightforward long context work over text and images, either model will do, and you can decide on the basis of which provider's output style you prefer in your own evaluations.
Calling either one through api14 works the same way because the API is OpenAI compatible. You point your client at the api14 base URL, send your key, and use the chat completions shape you already know. The only thing that changes between the two models is the model id string: use google/gemini-3.1-flash-lite-preview for the Gemini model and qwen/qwen3.5-plus-02-15 for the Qwen model. Everything else, the messages array, the role structure, the tool definitions, the response parsing, stays identical. That means you can keep both behind a single configuration value and switch per request or per environment without touching the rest of your integration.
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/qwen3.5-plus-02-15" and prompts, streaming and tools keep working.
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
- LiquidAI: LFM3-1.7B vs Ricursive: Ripple 65B Preview
- LiquidAI: LFM3-1.7B vs Crouton Alpha
- LiquidAI: LFM3-1.7B vs Tencent: Hy4
- LiquidAI: LFM3-1.7B vs Z.ai: GLM 5.3 FlashX Legal
- Ricursive: Ripple 65B Preview vs Crouton Alpha
- Ricursive: Ripple 65B Preview vs Tencent: Hy4
- Ricursive: Ripple 65B Preview vs Z.ai: GLM 5.3 FlashX Legal
- Crouton Alpha vs Tencent: Hy4