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UI-TARS 7B vs Ling 3.1 Flash: API price, context & features · api14

ByteDance: UI-TARS 7B vs inclusionAI: Ling 3.1 Flash

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

Specifications

	ByteDance: UI-TARS 7B	inclusionAI: Ling 3.1 Flash
Provider	ByteDance	inclusionAI
Input	$0.35 / 1M tokens	$0.16 / 1M tokens
Output	$0.70 / 1M tokens	$0.47 / 1M tokens
Cache read	$0.35 / 1M tokens	$0.03 / 1M tokens
Context window	128,000	1,000,000
Max output	2,048	262,144
Input	image, text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 22, 2025	Oct 2, 2026

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 7.8× more context (1,000,000 tokens).
Only inclusionAI: Ling 3.1 Flash supports tool calling.
Only inclusionAI: Ling 3.1 Flash supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.000700 with ByteDance: UI-TARS 7B and $0.000394 with inclusionAI: Ling 3.1 Flash.

In depth

ByteDance: UI-TARS 7B is a text-output model from ByteDance, released on 2025-07-22. It accepts both images and text as input, which places it in the small group of models that can look at a screenshot or a picture alongside a prompt. inclusionAI: Ling 3.1 Flash comes from inclusionAI, released on 2026-10-02, and it is a text-in, text-out model. Both are listed on api14 under the text category, so neither is a dedicated embedding or image-generation endpoint.

The clearest split between them is context and modality. Ling 3.1 Flash carries a context window of 1,000,000 tokens, far larger than the 128,000 tokens offered by UI-TARS 7B, so Ling is the one to reach for when you need to feed in very long documents or long conversation histories in a single call. UI-TARS 7B is the only one of the two that accepts image input, so any task involving visual material has to go to it. On tooling, Ling 3.1 Flash is the only one exposing tools and tool_choice, and it is also the only one exposing reasoning and include_reasoning, so agent-style workflows and explicit reasoning control belong to Ling. Both support structured_outputs, so that capability does not separate them. On price, Ling 3.1 Flash is the cheaper option for input tokens, while UI-TARS 7B is the cheaper option for output tokens, so the cost picture depends on the shape of your traffic.

For practical selection, start with the input. If your request includes an image, UI-TARS 7B is the pick, since Ling 3.1 Flash cannot accept one. If your request is pure text and long, Ling 3.1 Flash is the pick, because its context window is roughly eight times larger and it will not truncate a large document the way UI-TARS 7B would. If you need the model to call functions or tools, or you want reasoning output surfaced, Ling 3.1 Flash is again the pick, as UI-TARS 7B does not expose those parameters. If your workload is short text prompts with long generated answers, UI-TARS 7B has the cheaper output side, which may matter more than the input side depending on your generation-to-prompt ratio. Both accept temperature, top_p, top_k, seed, stop, frequency_penalty, presence_penalty, repetition_penalty, logit_bias, logprobs and top_logprobs, so sampling and log-probability controls are available on either.

Calling either model through api14 uses the same OpenAI-compatible surface. You point your client at the api14 base URL, send your key, and set the model field to the id you want. For UI-TARS 7B that id is "bytedance/ui-tars-1.5-7b"; for Ling 3.1 Flash it is "inclusionai/ling-3.1-flash". The request body shape stays the same, and switching between the two is a one-line change to the model string. The main caveat is that parameters are not identical across the two: if you send tools or reasoning to UI-TARS 7B, or an image to Ling 3.1 Flash, the call will not behave as it would on the other model, so keep your request builder aware of which id is in play.

There are cases where the model that looks weaker on paper is still the better call. If your task is screenshot understanding, form reading, or any visual grounding, Ling 3.1 Flash is not an option at all regardless of its larger context, so UI-TARS 7B wins by default. Likewise, if you are generating long text from short prompts and output tokens dominate your bill, UI-TARS 7B's cheaper output side can make it the more economical choice even though Ling has the cheaper input side.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "bytedance/ui-tars-1.5-7b" to "inclusionai/ling-3.1-flash" and prompts, streaming and tools keep working.

ByteDance: UI-TARS 7B details & playground →inclusionAI: Ling 3.1 Flash 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": "inclusionai/ling-3.1-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

ByteDance: UI-TARS 7B vs inclusionAI: Ling 3.1 Flash

Specifications

Key differences

In depth

Switch with one string

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