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.

Qwen3 VL 8B Instruct vs ReMM SLERP 13B: API price, context & features · api14

Qwen: Qwen3 VL 8B Instruct vs ReMM SLERP 13B

A side-by-side comparison of Qwen: Qwen3 VL 8B Instruct ($0.88 / 1M tokens) and ReMM SLERP 13B ($1.22 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Qwen: Qwen3 VL 8B Instruct	ReMM SLERP 13B
Provider	Qwen	Undi95
Input	$0.88 / 1M tokens	$1.22 / 1M tokens
Output	$2.63 / 1M tokens	$2.28 / 1M tokens
Cache read	$0.42 / 1M tokens	-
Context window	262,144	6,144
Max output	235,929	5,529
Input	image, text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 14, 2025	Jul 22, 2023

Key differences

Qwen: Qwen3 VL 8B Instruct is 29% cheaper on input tokens.
ReMM SLERP 13B is 13% cheaper on output tokens.
Qwen: Qwen3 VL 8B Instruct reads 42.7× more context (262,144 tokens).
Only Qwen: Qwen3 VL 8B Instruct supports tool calling.
A request with 1,000 input and 500 output tokens costs $0.00219 with Qwen: Qwen3 VL 8B Instruct and $0.00236 with ReMM SLERP 13B.

In depth

Qwen: Qwen3 VL 8B Instruct is made by Qwen, and ReMM SLERP 13B is made by Undi95. Both are text output models, so whatever you send in, you get text back. The Qwen model was released on 2025-10-14, while ReMM SLERP 13B dates back to 2023-07-22. That age gap matters less than the feature gap, but it is worth knowing that one is a recent vision language model and the other is an older text only merge.

The biggest practical difference is modality and context. Qwen3 VL 8B Instruct accepts both image and text input, so it can look at a screenshot, a diagram or a photo alongside your prompt. ReMM SLERP 13B accepts text only. Qwen3 VL 8B Instruct also has a far larger context window at 262144 tokens, compared with 6144 tokens for ReMM SLERP 13B, so long documents, large code files or extended chat histories fit comfortably in the Qwen model and will not fit in the other. On tooling, only Qwen3 VL 8B Instruct supports tools, which means function calling and agent style workflows are available there and not on ReMM SLERP 13B. Both expose structured outputs, so JSON style responses are possible on either. Neither is listed as a dedicated reasoning model. On price, Qwen3 VL 8B Instruct is the cheaper option for input, while ReMM SLERP 13B is cheaper for output, so the cost balance depends on how much you send versus how much you generate.

For most new work, Qwen3 VL 8B Instruct is the safer default. Pick it when your input includes images, when your prompt or conversation is long, or when you need tool calls and structured outputs in the same request. It is also the better choice when input tokens dominate your bill, since it is cheaper on that side. ReMM SLERP 13B makes sense when you are doing pure text generation, your prompts and outputs are short enough to stay inside 6144 tokens, and you do not need tools or image understanding. Because its output is cheaper, it can be attractive for workloads that are mostly generation with small prompts, such as short creative writing, simple chat replies or bulk text transformations.

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 your key, and set the model field. The only change between the two is that model id string: use "qwen/qwen3-vl-8b-instruct" for the Qwen model and "undi95/remm-slerp-l2-13b" for ReMM SLERP 13B. Your request body, chat messages and parameter names stay the same, so switching is a one line edit. If you use a client library, set the model in your config or environment variable rather than hard coding it, so you can flip between them without touching request logic.

There are still cases where ReMM SLERP 13B is the better pick. If your task is plain text with no images, no tools and short context, and your usage is output heavy, its cheaper output and its extra sampling controls such as min_p and top_a give you tuning options the Qwen model does not list. In that narrow shape of workload, the older model can be the more economical and more controllable choice.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "qwen/qwen3-vl-8b-instruct" to "undi95/remm-slerp-l2-13b" and prompts, streaming and tools keep working.

Qwen: Qwen3 VL 8B Instruct details & playground →ReMM SLERP 13B 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": "undi95/remm-slerp-l2-13b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Qwen: Qwen3 VL 8B Instruct vs ReMM SLERP 13B

Specifications

Key differences

In depth

Switch with one string

More comparisons