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Command R (08-2024) vs MiniMax M3.1 Flash: API price, context & features · api14

Cohere: Command R (08-2024) vs MiniMax: MiniMax M3.1 Flash

A side-by-side comparison of Cohere: Command R (08-2024) ($0.52 / 1M tokens) and MiniMax: MiniMax M3.1 Flash ($0.52 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Cohere: Command R (08-2024)	MiniMax: MiniMax M3.1 Flash
Provider	Cohere	MiniMax
Input	$0.52 / 1M tokens	$0.52 / 1M tokens
Output	$2.10 / 1M tokens	$2.10 / 1M tokens
Cache read	-	$0.05 / 1M tokens
Context window	128,000	1,048,576
Max output	4,000	524,288
Input	text	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Aug 30, 2024	Oct 19, 2026

Key differences

Both have about the same input price.
MiniMax: MiniMax M3.1 Flash reads 8.2× more context (1,048,576 tokens).
Only MiniMax: MiniMax M3.1 Flash supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00158 with Cohere: Command R (08-2024) and $0.00158 with MiniMax: MiniMax M3.1 Flash.

In depth

Cohere: Command R (08-2024) is a text model from Cohere, released on 2024-08-30. MiniMax: MiniMax M3.1 Flash is a text model from MiniMax, released on 2026-10-19. Both sit in the text category and both return text output, so at the level of basic purpose they are aimed at the same kind of work: generating and transforming language through an API.

The clearest split is in context and input modalities. Command R accepts text only, with a context window of 128,000 tokens. MiniMax M3.1 Flash accepts text, image and video input, and its context window is 1,048,576 tokens, which is substantially larger. MiniMax M3.1 Flash also exposes reasoning controls (include_reasoning, reasoning, reasoning_effort) that Command R does not list, so it is the only one of the two with an explicit reasoning mode. On tool calling and structured outputs the two are effectively level, since both list tools, tool_choice and structured_outputs among their supported parameters. MiniMax M3.1 Flash is the cheaper of the two on both input and output, and it is the one with the bigger context.

For day to day text work, either model will do, and the choice comes down to what you are feeding in and how much you want to pay. If your task is plain text generation, summarisation, extraction or tool driven workflows over documents that fit comfortably in 128,000 tokens, Command R is a reasonable, well specified option with the parameters most teams expect. If you need to pass images or video alongside text, or you are working with very long inputs that would otherwise need chunking, MiniMax M3.1 Flash is the better fit because it accepts those modalities and holds far more context in one call. If you want the model to expose its reasoning steps or you want to tune reasoning effort, that is another reason to pick MiniMax M3.1 Flash. Cost points the same way, since MiniMax M3.1 Flash is cheaper on both input and output.

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 request to the chat completions endpoint with your api14 key, and set the model field to the id you want. To use Command R, set the model to "cohere/command-r-08-2024". To use MiniMax M3.1 Flash, set the model to "minimax/minimax-m3.1-flash". Nothing else in the request shape has to change for the basic case: same messages array, same role structure, same streaming option. You can keep one code path and switch models by changing that single string, which makes it easy to compare outputs or fall back from one to the other.

There are still cases where Command R is the better pick. If your workload is text only, fits inside 128,000 tokens, and you do not need reasoning controls, the extra modalities and the much larger context of MiniMax M3.1 Flash buy you nothing, and a smaller, simpler parameter surface can be easier to reason about when you are tuning temperature, top_p, penalties and stop sequences. Both models support the same core sampling and tool parameters, so if your pipeline already targets those, Command R will slot in without changes. The tradeoff is straightforward: pick MiniMax M3.1 Flash when you need images, video, a very large context, reasoning controls or lower cost, and pick Command R when your inputs are text and modest in size and you prefer the narrower, more familiar parameter set.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "cohere/command-r-08-2024" to "minimax/minimax-m3.1-flash" and prompts, streaming and tools keep working.

Cohere: Command R (08-2024) details & playground →MiniMax: MiniMax M3.1 Flash 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-m3.1-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Cohere: Command R (08-2024) vs MiniMax: MiniMax M3.1 Flash

Specifications

Key differences

In depth

Switch with one string

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