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.

Llama 3.1 70B Instruct vs UnslopNemo 12B: API price, context & features · api14

Meta: Llama 3.1 70B Instruct vs TheDrummer: UnslopNemo 12B

A side-by-side comparison of Meta: Llama 3.1 70B Instruct ($2.52 / 1M tokens) and TheDrummer: UnslopNemo 12B ($1.40 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Llama 3.1 70B Instruct	TheDrummer: UnslopNemo 12B
Provider	Meta	TheDrummer
Input	$2.52 / 1M tokens	$1.40 / 1M tokens
Output	$2.52 / 1M tokens	$1.40 / 1M tokens
Context window	131,072	1,024,000
Max output	8,192	819,200
Input	text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 23, 2024	Nov 8, 2024

Key differences

TheDrummer: UnslopNemo 12B is 44% cheaper on input tokens.
TheDrummer: UnslopNemo 12B is 44% cheaper on output tokens.
TheDrummer: UnslopNemo 12B reads 7.8× more context (1,024,000 tokens).
Only TheDrummer: UnslopNemo 12B supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00378 with Meta: Llama 3.1 70B Instruct and $0.00210 with TheDrummer: UnslopNemo 12B.

In depth

Meta: Llama 3.1 70B Instruct is a text model from Meta, released on 2024-07-23. TheDrummer: UnslopNemo 12B is a text model from TheDrummer, released on 2024-11-08. Both are served through api14 as chat completion models, and both take text in and return text out, so neither one handles images or audio. The naming tells you most of what you need at a glance: one is a 70B general purpose instruction tuned model from a large lab, the other is a 12B community fine tune aimed at a particular style of output.

The clearest technical difference is context length. UnslopNemo 12B accepts up to 1,024,000 tokens, while Llama 3.1 70B Instruct accepts up to 131,072 tokens. If your prompt or retrieved document set is very large, UnslopNemo is the one that will fit it without chunking. The other meaningful difference is tooling. Llama 3.1 70B Instruct supports tools and tool_choice, so it can drive function calling and agent style loops directly. UnslopNemo 12B does not list those parameters, so it is not the model to reach for when you need the model itself to emit structured tool calls. On price the two are about the same, so cost is not the deciding factor here. Both support structured_outputs, temperature, top_p, top_k, repetition_penalty, presence_penalty, frequency_penalty, logit_bias, seed and stop, so sampling and output shaping work the same way on either side.

For day to day work, pick based on what the task actually needs. If you are building an agent, a retrieval pipeline that has to call functions, or anything that needs the model to decide which tool to invoke, Llama 3.1 70B Instruct is the natural choice because it is the only one of the two with tool support. If you are doing long document summarization, stuffing a large codebase or transcript into a single request, or working on creative and conversational writing where you want a lighter model with a very large window, UnslopNemo 12B is the better fit. For plain instruction following, classification, extraction or rewriting where the input fits comfortably in 131,072 tokens and no tools are involved, either model will do the job, and you can choose on output style rather than capability.

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 normal chat completions request with your messages array, and set the model field. To use Llama 3.1 70B Instruct, set model to meta-llama/llama-3.1-70b-instruct. To use UnslopNemo 12B, set model to thedrummer/unslopnemo-12b. Everything else in the request body, including temperature, max_tokens, stop and structured_outputs, stays as it is. Switching between the two is a one line change, so you can keep both behind a config value and route per request without rewriting your integration.

There is one case worth calling out in the other direction. Even though UnslopNemo 12B has the much larger context window, it does not support tools, so if a long context task also needs function calling, Llama 3.1 70B Instruct is still the right pick despite the smaller window. You would then need to chunk your input to stay within 131,072 tokens rather than give up tool use.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta-llama/llama-3.1-70b-instruct" to "thedrummer/unslopnemo-12b" and prompts, streaming and tools keep working.

Meta: Llama 3.1 70B Instruct details & playground →TheDrummer: UnslopNemo 12B 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": "thedrummer/unslopnemo-12b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Llama 3.1 70B Instruct vs TheDrummer: UnslopNemo 12B

Specifications

Key differences

In depth

Switch with one string

More comparisons