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Llama 3.3 70B Instruct vs GPT-4.1 Nano: API price, context & features · api14

Meta: Llama 3.3 70B Instruct vs OpenAI: GPT-4.1 Nano

A side-by-side comparison of Meta: Llama 3.3 70B Instruct ($2.07 / 1M tokens) and OpenAI: GPT-4.1 Nano ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Llama 3.3 70B Instruct	OpenAI: GPT-4.1 Nano
Provider	Meta	OpenAI
Input	$2.07 / 1M tokens	$0.35 / 1M tokens
Output	$2.77 / 1M tokens	$1.40 / 1M tokens
Cache read	$1.03 / 1M tokens	$0.11 / 1M tokens
Web search	-	$35.00 / 1K searches
Context window	131,072	1,047,576
Max output	32,768	942,818
Input	text	image, text, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Dec 6, 2024	Apr 14, 2025

Key differences

OpenAI: GPT-4.1 Nano is 83% cheaper on input tokens.
OpenAI: GPT-4.1 Nano is 49% cheaper on output tokens.
OpenAI: GPT-4.1 Nano reads 8.0× more context (1,047,576 tokens).
A request with 1,000 input and 500 output tokens costs $0.00345 with Meta: Llama 3.3 70B Instruct and $0.00105 with OpenAI: GPT-4.1 Nano.

In depth

Meta: Llama 3.3 70B Instruct is a text model from Meta, released on 2024-12-06. OpenAI: GPT-4.1 Nano is a text model from OpenAI, released on 2025-04-14. Both are served on api14 under the text category, and both take text in and return text out, so at the level of basic shape they are the same kind of thing: a model you send a prompt to and get a completion from.

The differences show up in context and modalities. GPT-4.1 Nano has the bigger context window, at 1047576 tokens against 131072 for Llama 3.3 70B Instruct, so if your prompt or your retrieved context is very large, Nano is the one that will fit it. Nano also accepts image and file input alongside text, while Llama 3.3 70B Instruct accepts text only. On the parameter side, Llama 3.3 70B Instruct exposes a wider set of sampling and control knobs, including frequency_penalty, logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, stop, top_k and top_logprobs, which Nano does not list. Nano's list is narrower: max_completion_tokens, response_format, seed, structured_outputs, temperature, tool_choice, tools and top_p. Both support tools and structured outputs, so neither has an exclusive claim there.

For practical selection, reach for GPT-4.1 Nano when the job involves long documents, image or file input, or when you want a large context to hold a lot of material in one call. Reach for Llama 3.3 70B Instruct when you need fine grained control over generation, for example logprobs or top_logprobs for scoring and analysis, logit_bias to steer token choices, or penalties and top_k to shape sampling. On price, input cost is about the same between the two, while output is cheaper on Llama 3.3 70B Instruct, so workloads with long generations lean that way. Check the live price table for the actual numbers.

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. To switch models you change only that field, between "meta-llama/llama-3.3-70b-instruct" and "openai/gpt-4.1-nano". Your request and response handling stay as they are, with one caveat: if you use parameters that only one model lists, such as logprobs or top_k on the Llama side, drop them when you move to Nano, and if you use max_completion_tokens on the Nano side, map it appropriately when you move to Llama.

There are cases where the model you did not pick is still the better call. If you are on GPT-4.1 Nano for its long context but your task is short and text only, and you want sampling controls or cheaper output, Llama 3.3 70B Instruct is the better fit. If you are on Llama 3.3 70B Instruct but your input includes an image or a file, or your context runs past 131072 tokens, Nano is the one that can handle it.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta-llama/llama-3.3-70b-instruct" to "openai/gpt-4.1-nano" and prompts, streaming and tools keep working.

Meta: Llama 3.3 70B Instruct details & playground →OpenAI: GPT-4.1 Nano 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": "openai/gpt-4.1-nano",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Llama 3.3 70B Instruct vs OpenAI: GPT-4.1 Nano

Specifications

Key differences

In depth

Switch with one string

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