We use cookies. Cookies and browser storage keep you signed in and remember your preferences. With your permission we also measure how the site is used. You can any time. See our cookie policy.

New here? See how it works A 1-minute tour of the playground, models and your API key.

Ling 3.1 Flash vs Qwen2.5 7B Instruct: API price, context & features · api14

inclusionAI: Ling 3.1 Flash vs Qwen: Qwen2.5 7B Instruct

A side-by-side comparison of inclusionAI: Ling 3.1 Flash ($0.16 / 1M tokens) and Qwen: Qwen2.5 7B Instruct ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	inclusionAI: Ling 3.1 Flash	Qwen: Qwen2.5 7B Instruct
Provider	inclusionAI	Qwen
Input	$0.16 / 1M tokens	$0.35 / 1M tokens
Output	$0.47 / 1M tokens	$0.70 / 1M tokens
Cache read	$0.03 / 1M tokens	-
Context window	1,000,000	32,768
Max output	262,144	29,491
Input	text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 2, 2026	Oct 16, 2024

Key differences

inclusionAI: Ling 3.1 Flash is 55% cheaper on input tokens.
inclusionAI: Ling 3.1 Flash is 32% cheaper on output tokens.
inclusionAI: Ling 3.1 Flash reads 30.5× more context (1,000,000 tokens).
Only inclusionAI: Ling 3.1 Flash supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.000394 with inclusionAI: Ling 3.1 Flash and $0.000700 with Qwen: Qwen2.5 7B Instruct.

In depth

inclusionAI: Ling 3.1 Flash is a text model from inclusionAI, released on 2026-10-02. Qwen: Qwen2.5 7B Instruct is a text model from Qwen, released on 2024-10-16. Both are served through api14 as chat models in the text category, so at the level of basic use they occupy the same slot in your stack: you send messages, you get text back. The differences that matter for a routing decision are context size, the parameter surface each one exposes, and price direction.

The clearest split is context. Ling 3.1 Flash accepts up to 1,000,000 tokens, while Qwen2.5 7B Instruct accepts 32,768. That is a large gap, and it changes what you can put in a single request: whole repositories, long document sets, or extended conversation histories fit in Ling 3.1 Flash but will need chunking or retrieval on Qwen2.5 7B Instruct. Both models are text in and text out, so neither adds image or audio handling. On parameters, Ling 3.1 Flash supports reasoning and include_reasoning, which Qwen2.5 7B Instruct does not, so step by step internal reasoning is only available on the Ling side. Both support tools and tool_choice, and both support structured_outputs and response_format, so function calling and schema constrained output are available either way. Ling 3.1 Flash also exposes logit_bias, logprobs and top_logprobs, which Qwen2.5 7B Instruct does not, which matters if you inspect token level probabilities. On price, Ling 3.1 Flash is the cheaper of the two for input, while Qwen2.5 7B Instruct is the cheaper of the two for output.

For task selection, start from the input you need to send. If your prompt is a large corpus, a long transcript, or anything that would otherwise require a retrieval layer just to fit, Ling 3.1 Flash is the natural pick because of the context window, and its reasoning support helps on multi step problems where the model benefits from working through intermediate steps. If your workload is short prompts with long generated answers, the output side of the price matters more, and Qwen2.5 7B Instruct is the cheaper option there. Qwen2.5 7B Instruct is also a reasonable default for straightforward instruction following, extraction, classification and short form generation where a 32,768 token window is plenty and you do not need reasoning traces or token level log probabilities. Neither model is restricted to a single use case by modality, since both are text only, so the decision comes down to context, reasoning and which side of the token bill dominates your traffic.

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 standard chat completions request with your messages array, and set the model field. The only change between the two is that string: use inclusionai/ling-3.1-flash for Ling 3.1 Flash and qwen/qwen-2.5-7b-instruct for Qwen2.5 7B Instruct. Your existing OpenAI SDK code, your message format and your response parsing stay as they are. If you use tools, structured outputs or sampling parameters, pass them in the request body as usual, keeping in mind that reasoning, logit_bias and logprobs apply only to the Ling model. Because the model id is the single switch, you can keep both behind one interface and route per request, or run one as a fallback for the other, without maintaining two client implementations.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "inclusionai/ling-3.1-flash" to "qwen/qwen-2.5-7b-instruct" and prompts, streaming and tools keep working.

inclusionAI: Ling 3.1 Flash details & playground →Qwen: Qwen2.5 7B 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-2.5-7b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

inclusionAI: Ling 3.1 Flash vs Qwen: Qwen2.5 7B Instruct

Specifications

Key differences

In depth

Switch with one string

More comparisons