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Llama 3.1 70B Instruct vs Qwen2.5 72B Instruct: API price, context & features · api14

Meta: Llama 3.1 70B Instruct vs Qwen2.5 72B Instruct

A side-by-side comparison of Meta: Llama 3.1 70B Instruct ($2.52 / 1M tokens) and Qwen2.5 72B Instruct ($1.26 / 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	Qwen2.5 72B Instruct
Provider	Meta	Qwen
Input	$2.52 / 1M tokens	$1.26 / 1M tokens
Output	$2.52 / 1M tokens	$1.40 / 1M tokens
Context window	131,072	32,768
Max output	8,192	16,384
Input	text	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 23, 2024	Sep 19, 2024

Key differences

Qwen2.5 72B Instruct is 50% cheaper on input tokens.
Qwen2.5 72B Instruct is 44% cheaper on output tokens.
Meta: Llama 3.1 70B Instruct reads 4.0× more context (131,072 tokens).
Only Qwen2.5 72B Instruct supports tool calling.
Only Qwen2.5 72B Instruct 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 $0.00196 with Qwen2.5 72B Instruct.

In depth

Meta: Llama 3.1 70B Instruct is a text model from Meta, released on 2024-07-23. Qwen2.5 72B Instruct is a text model from Qwen, released on 2024-09-19. Both sit in the same rough weight class, roughly seventy billion parameters, and both are instruction tuned for chat and general assistant work. Neither is a small model, so both are aimed at developers who want strong general language behaviour rather than a cheap high volume workhorse.

The clearest difference is context. Llama 3.1 70B Instruct accepts up to 131072 tokens, which is four times the 32768 token window of Qwen2.5 72B Instruct. If your prompt includes long documents, large codebases, long transcripts or many retrieved chunks, that gap matters and Llama is the one that will fit the input. On modalities the two are identical: text in, text out, no image or audio handling on either side. Their supported parameter lists also match exactly, so both expose temperature, top_p, top_k, min_p, repetition_penalty, frequency_penalty, presence_penalty, logit_bias, seed, stop, max_tokens, response_format, structured_outputs, tools and tool_choice. There is no capability one has that the other lacks in that list, so the choice does not come down to feature support.

For practical selection, start with the input size. Long context retrieval, whole file review, multi document summarisation and anything where you would otherwise chunk aggressively point to Llama 3.1 70B Instruct, simply because the window is larger. For shorter, well scoped tasks such as chat turns, single function calls, classification, extraction from a paragraph or two, and agent steps where each request is small, Qwen2.5 72B Instruct is a reasonable default, and it is the cheaper of the two on input. Output pricing is about the same for both, so the input side is where the cost difference shows up. If your workload is mostly short prompts, that input advantage for Qwen adds up; if your prompts are long, Llama's larger window may save you from splitting requests at all, which can matter more than the per token rate.

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 use Llama, set the model id to meta-llama/llama-3.1-70b-instruct. To use Qwen, set it to qwen/qwen-2.5-72b-instruct. Everything else in the request body, messages, temperature, max_tokens, tools, response_format and the rest, stays as it is. Switching between the two is a one line change, so you can benchmark both against your own prompts and pick per route or per task without rewriting your integration.

There is one case where Qwen2.5 72B Instruct is still the better pick even when context is not tight: when cost on input is the deciding factor and your prompts fit comfortably inside 32768 tokens. In that situation the larger window of Llama buys you nothing you will use, and Qwen is the cheaper input option. Conversely, if you ever expect to grow into longer inputs, Llama 3.1 70B Instruct gives you headroom without a second migration later.

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 "qwen/qwen-2.5-72b-instruct" and prompts, streaming and tools keep working.

Meta: Llama 3.1 70B Instruct details & playground →Qwen2.5 72B 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-2.5-72b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Llama 3.1 70B Instruct vs Qwen2.5 72B Instruct

Specifications

Key differences

In depth

Switch with one string

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