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Kimi K2.6 vs Hermes 4 405B: API price, context & features · api14

MoonshotAI: Kimi K2.6 vs Nous: Hermes 4 405B

A side-by-side comparison of MoonshotAI: Kimi K2.6 ($2.28 / 1M tokens) and Nous: Hermes 4 405B ($3.50 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	MoonshotAI: Kimi K2.6	Nous: Hermes 4 405B
Provider	Moonshot AI	Nous
Input	$2.28 / 1M tokens	$3.50 / 1M tokens
Output	$11.94 / 1M tokens	$10.50 / 1M tokens
Cache read	$0.52 / 1M tokens	$0.70 / 1M tokens
Context window	262,144	131,072
Max output	235,929	117,964
Input	text, image	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 20, 2026	Aug 26, 2025

Key differences

MoonshotAI: Kimi K2.6 is 35% cheaper on input tokens.
Nous: Hermes 4 405B is 12% cheaper on output tokens.
MoonshotAI: Kimi K2.6 reads 2.0× more context (262,144 tokens).
Only MoonshotAI: Kimi K2.6 supports tool calling.
A request with 1,000 input and 500 output tokens costs $0.00824 with MoonshotAI: Kimi K2.6 and $0.00875 with Nous: Hermes 4 405B.

In depth

MoonshotAI: Kimi K2.6 is a text model from Moonshot AI, released on 2026-04-20. Nous: Hermes 4 405B is a text model from Nous, released on 2025-08-26. Both are served through api14 as chat models with text output, so from the caller's point of view they are the same kind of endpoint, just backed by different weights and different providers.

The clearest split is context and input modality. Kimi K2.6 accepts a context of 262144 tokens and takes both text and image input, while Hermes 4 405B is capped at 131072 tokens and takes text only. So if you need to feed in a long document, a large codebase slice, or a screenshot alongside your prompt, Kimi K2.6 is the one that can do it. On parameters, Kimi K2.6 exposes a wider surface, including tools, tool_choice, parallel_tool_calls, logit_bias, min_p and seed, none of which appear on Hermes 4 405B. Both models list reasoning and include_reasoning, and both list structured_outputs and response_format, so neither has a clear edge on reasoning or structured output based on what is declared. On price, Kimi K2.6 is the cheaper of the two for input, while Hermes 4 405B is cheaper for output, so the better deal depends on whether your workload is input heavy or output heavy.

For practical selection, reach for Kimi K2.6 when the task involves images, very long inputs, or tool calling. Agent loops, retrieval over big documents, and anything that needs to call functions or run parallel tool calls fit it well. Hermes 4 405B is a reasonable pick when your prompts and completions are plain text, your context fits comfortably under 131072 tokens, and your workload is dominated by generated output rather than consumed input, since its output side is the cheaper one. If you are unsure, the input side of Kimi K2.6 being cheaper makes it the safer default for chat and analysis tasks where you paste in a lot of material.

Calling either model through api14 is the same operation. Both are OpenAI compatible, so you point your existing client at the api14 base URL, send a request to the chat completions endpoint, and set the model field. To use Kimi K2.6, set the model id to "moonshotai/kimi-k2.6". To use Hermes 4 405B, set it to "nousresearch/hermes-4-405b". Nothing else in the request shape has to change. You can keep one code path and swap the string, which makes A/B testing between them cheap. Just remember that if you switch to Hermes 4 405B you lose image input and the tool related parameters, so strip those from the request or guard them behind a capability check.

There is still a case for Hermes 4 405B even when Kimi K2.6 looks like the stronger option. If your application is output heavy, for example long form generation, summarisation that produces lengthy text, or batch jobs where the completion is much larger than the prompt, the cheaper output side matters more than the cheaper input side, and Hermes 4 405B becomes the more economical choice. It also fits workloads that are purely text and comfortably within its context limit, where the extra context and image support of Kimi K2.6 would go unused.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "moonshotai/kimi-k2.6" to "nousresearch/hermes-4-405b" and prompts, streaming and tools keep working.

MoonshotAI: Kimi K2.6 details & playground →Nous: Hermes 4 405B 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": "nousresearch/hermes-4-405b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

MoonshotAI: Kimi K2.6 vs Nous: Hermes 4 405B

Specifications

Key differences

In depth

Switch with one string

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