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Muse Glimmer 30B vs MiniMax M2.7: API price, context & features · api14

Meta: Muse Glimmer 30B vs MiniMax: MiniMax M2.7

A side-by-side comparison of Meta: Muse Glimmer 30B ($1.05 / 1M tokens) and MiniMax: MiniMax M2.7 ($0.74 / 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	MiniMax: MiniMax M2.7
Provider	Meta	MiniMax
Input	$1.05 / 1M tokens	$0.74 / 1M tokens
Output	$4.20 / 1M tokens	$2.94 / 1M tokens
Cache read	$0.14 / 1M tokens	$0.15 / 1M tokens
Context window	131,072	204,800
Max output	16,384	176,947
Input	text, image	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Aug 9, 2026	Mar 18, 2026

Key differences

MiniMax: MiniMax M2.7 is 30% cheaper on input tokens.
MiniMax: MiniMax M2.7 is 30% cheaper on output tokens.
MiniMax: MiniMax M2.7 reads 1.6× more context (204,800 tokens).
Only Meta: Muse Glimmer 30B supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00315 with Meta: Muse Glimmer 30B and $0.00221 with MiniMax: MiniMax M2.7.

In depth

Meta: Muse Glimmer 30B is a text model from Meta, released on 2026-08-09. MiniMax: MiniMax M2.7 is a text model from MiniMax, released on 2026-03-18. Both are served as text category models on api14, and both accept text as input and return text as output. The main structural difference between them is how much context they can hold and what kinds of input they accept.

MiniMax M2.7 has the larger context window at 204800 tokens, while Muse Glimmer 30B sits at 131072 tokens. Muse Glimmer 30B is the only one of the two that accepts image input alongside text, so if your prompt includes a screenshot, diagram or scanned page, that is the model to reach for. MiniMax M2.7 is text in, text out only. On price, MiniMax M2.7 is the cheaper option for both input and output, so it is the more economical choice when you are pushing a lot of tokens through. Muse Glimmer 30B is the only one of the two that lists structured_outputs among its supported parameters, which matters if you need the model to conform to a schema. Both models expose tools, tool_choice, reasoning, include_reasoning, response_format, seed, stop, temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, logit_bias and max_tokens, so the everyday sampling and tool-calling surface is the same on either side.

For practical work, the split is fairly clean. If your task is long document analysis, large codebase review, or any workload where you are feeding in very large text payloads and watching cost, MiniMax M2.7 is the better default because of the bigger context and the lower input and output price. If your task involves an image, or if you need the response constrained to a structured output schema, Muse Glimmer 30B is the one that supports it. For ordinary chat, summarisation, extraction and tool-calling over text, either model will do the job, and the deciding factors come down to context size, image support, structured output support and price.

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 usual chat completions request, and set the model field to the id you want. To use Muse Glimmer 30B, set the model to meta/muse-glimmer-30b. To use MiniMax M2.7, set the model to minimax/minimax-m2.7. Everything else in the request body, your messages, temperature, tools, response_format and the rest, stays as it is. Switching between the two is a one-line change to the model id, so you can benchmark both against your own workload without rewriting your integration.

There are still cases where the other model wins. If you are working entirely in text and your prompts fit comfortably inside 131072 tokens, MiniMax M2.7 remains the cheaper pick for both input and output, so there is no reason to pay more for image support or structured outputs you are not using. Conversely, if you need image input or schema-constrained output, Muse Glimmer 30B is the only one of the pair that offers it, regardless of price.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-glimmer-30b" to "minimax/minimax-m2.7" and prompts, streaming and tools keep working.

Meta: Muse Glimmer 30B details & playground →MiniMax: MiniMax M2.7 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": "minimax/minimax-m2.7",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Glimmer 30B vs MiniMax: MiniMax M2.7

Specifications

Key differences

In depth

Switch with one string

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