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Meta: Muse Glimmer 30B vs OpenAI: GPT-5.4 Nano

A side-by-side comparison of Meta: Muse Glimmer 30B ($1.05 / 1M tokens) and OpenAI: GPT-5.4 Nano ($0.70 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Muse Glimmer 30B	OpenAI: GPT-5.4 Nano
Provider	Meta	OpenAI
Input	$1.05 / 1M tokens	$0.70 / 1M tokens
Output	$4.20 / 1M tokens	$4.38 / 1M tokens
Cache read	$0.14 / 1M tokens	$0.07 / 1M tokens
Web search	-	$35.00 / 1K searches
Context window	131,072	400,000
Max output	16,384	128,000
Input	text, image	file, image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Aug 9, 2026	Mar 17, 2026

Key differences

OpenAI: GPT-5.4 Nano is 33% cheaper on input tokens.
OpenAI: GPT-5.4 Nano reads 3.1× more context (400,000 tokens).
A request with 1,000 input and 500 output tokens costs $0.00315 with Meta: Muse Glimmer 30B and $0.00289 with OpenAI: GPT-5.4 Nano.

In depth

Meta: Muse Glimmer 30B and OpenAI: GPT-5.4 Nano are both text generation models sold through api14, but they come from different labs and carry different design priorities. Muse Glimmer 30B is made by Meta and was released on 2026-08-09. GPT-5.4 Nano is made by OpenAI and was released on 2026-03-17. Both sit in the text category and both produce text output, so neither is an image or audio generator. The interesting split is on the input side and in how much material each one can hold at once.

Capability wise, the biggest structural difference is the context window. GPT-5.4 Nano accepts up to 400,000 tokens, which is roughly three times what Muse Glimmer 30B accepts at 131,072 tokens. GPT-5.4 Nano also takes three input modalities: text, images and files. Muse Glimmer 30B takes text and images, but not files. On the parameter front, the two overlap on the essentials (tools, tool_choice, structured_outputs, response_format, reasoning, reasoning_effort, include_reasoning, seed), and neither model is listed as the only one supporting tools, structured output or reasoning. Muse Glimmer 30B exposes a wider sampling surface, including temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, logit_bias and stop. GPT-5.4 Nano instead exposes max_completion_tokens and verbosity, which suggests a tighter, more opinionated generation interface. If you rely on fine grained sampling control, Glimmer gives you more knobs. If you rely on feeding documents straight in, Nano is the one that accepts files.

For task selection, start with the shape of your input. If you are working with long context: large codebases, multi document analysis, long transcripts, or anything where a file upload is the natural way to hand over data, GPT-5.4 Nano is the more comfortable fit because of the larger window and file input. If your work is image plus text, both models can handle it, so the decision falls back to context size and pricing. If you want to tune sampling behaviour closely, or you are porting a pipeline that depends on top_k, min_p, logit_bias or repetition_penalty, Muse Glimmer 30B is the safer choice since those parameters are supported there. The cost picture matters too: input tokens are cheaper on GPT-5.4 Nano, while output pricing is about the same on both. The live price table on this page has the current numbers, so check it before you commit to a high volume workload, where input cost differences compound quickly.

Calling either model through api14 works the same way, because the endpoint is OpenAI compatible. You keep your request body, your messages array, your tools and your response_format exactly as they are, and you only change the value of the model field. Pass "meta/muse-glimmer-30b" to route to Meta's model, or pass "openai/gpt-5.4-nano" to route to OpenAI's. Nothing else about the call structure changes, which means you can A/B the two models on the same prompt set by swapping that single string. One practical caveat: if your request uses parameters that only one model supports, such as top_k or min_p on Glimmer, or verbosity and max_completion_tokens on Nano, you should build the parameter block per model rather than assuming both accept identical fields. The model id is the switch; the parameters are where the two diverge.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-glimmer-30b" to "openai/gpt-5.4-nano" and prompts, streaming and tools keep working.

Meta: Muse Glimmer 30B details & playground →OpenAI: GPT-5.4 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-5.4-nano",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

More comparisons

Muse Glimmer 30B vs GPT-5.4 Nano: API price, context & features · api14