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

Meta: Llama 3.1 70B Instruct vs Mistral: Saba

A side-by-side comparison of Meta: Llama 3.1 70B Instruct ($2.52 / 1M tokens) and Mistral: Saba ($0.70 / 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	Mistral: Saba
Provider	Meta	Mistral
Input	$2.52 / 1M tokens	$0.70 / 1M tokens
Output	$2.52 / 1M tokens	$2.10 / 1M tokens
Cache read	-	$0.07 / 1M tokens
Context window	131,072	32,768
Max output	8,192	26,214
Input	text	text, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 23, 2024	Feb 17, 2025

Key differences

Mistral: Saba is 72% cheaper on input tokens.
Mistral: Saba is 17% cheaper on output tokens.
Meta: Llama 3.1 70B Instruct reads 4.0× more context (131,072 tokens).
Only Mistral: Saba supports tool calling.
Only Mistral: Saba 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 $0.00175 with Mistral: Saba.

In depth

Meta: Llama 3.1 70B Instruct is a text model from Meta, released on 2024-07-23. Mistral: Saba is a text model from Mistral, released on 2025-02-17. Both are served through api14 as chat completions models, and both accept text prompts and return text. The main structural difference is that Saba also accepts file inputs, so you can pass a document alongside your prompt rather than pasting its contents into the message.

The two models diverge most clearly on context window and modalities. Llama 3.1 70B Instruct has a context length of 131072 tokens, while Saba has 32768 tokens, so Llama gives you roughly four times the room for long inputs. Saba is the only one of the two that takes file inputs; Llama 3.1 70B Instruct is text in, text out. On parameters, Llama exposes a wider set, including logit_bias, min_p, repetition_penalty, and top_k, which Saba does not list. Both support tools, tool_choice, structured_outputs, response_format, seed, stop, temperature, top_p, frequency_penalty, presence_penalty, and max_tokens, so function calling and JSON-style output are available on either side. On price, Saba is the cheaper option for input, while Llama 3.1 70B Instruct is the cheaper option for output, so the better deal depends on how much you send versus how much you generate.

For practical selection, reach for Llama 3.1 70B Instruct when your prompt or conversation is long, when you need the extra sampling controls such as logit_bias, min_p, repetition_penalty, or top_k, or when your workload is output heavy and the cheaper output rate matters more. Reach for Mistral: Saba when you want to hand the model a file directly instead of extracting its text yourself, when your inputs fit comfortably inside 32768 tokens, or when your workload is input heavy and the cheaper input rate matters more. If you are unsure, the context window is usually the deciding factor: anything that would exceed 32768 tokens has to go to Llama.

Calling either model through api14 works the same way because the API is OpenAI compatible. You point your existing OpenAI client at the api14 base URL, send your key, and set the model field to the id you want. To switch models you change only that string, between "meta-llama/llama-3.1-70b-instruct" and "mistralai/mistral-saba". The request shape, the messages array, and the response parsing stay identical, so you can move a call from one model to the other by editing a single line, or route between them at runtime based on input length or whether a file is attached.

There are cases where the other model is still the better pick even after you have chosen. If you start on Saba for its file input and cheaper input rate, but a request grows past 32768 tokens or you need logit_bias, min_p, repetition_penalty, or top_k, Llama 3.1 70B Instruct is the one that can serve it. If you start on Llama for its long context and cheaper output, but you would rather pass a document as a file than paste its text, Saba covers that. Neither model is strictly better; the choice follows your input size, whether you need file inputs, which sampling parameters you rely on, and whether your token spend leans toward input or output.

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 "mistralai/mistral-saba" and prompts, streaming and tools keep working.

Meta: Llama 3.1 70B Instruct details & playground →Mistral: Saba 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": "mistralai/mistral-saba",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Llama 3.1 70B Instruct vs Mistral: Saba

Specifications

Key differences

In depth

Switch with one string

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