MoonshotAI: Kimi K2.6 vs Relace: Relace Search
A side-by-side comparison of MoonshotAI: Kimi K2.6 ($2.28 / 1M tokens) and Relace: Relace Search ($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 | Relace: Relace Search | |
|---|---|---|
| Provider | Moonshot AI | Relace |
| Input | $2.28 / 1M tokens | $3.50 / 1M tokens |
| Output | $11.94 / 1M tokens | $10.50 / 1M tokens |
| Cache read | $0.52 / 1M tokens | - |
| Context window | 262,144 | 256,000 |
| Max output | 235,929 | 128,000 |
| Input | text, image | text |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Apr 20, 2026 | Dec 8, 2025 |
Key differences
- MoonshotAI: Kimi K2.6 is 35% cheaper on input tokens.
- Relace: Relace Search is 12% cheaper on output tokens.
- Only MoonshotAI: Kimi K2.6 supports structured JSON output.
- Only MoonshotAI: Kimi K2.6 supports reasoning effort control.
- A request with 1,000 input and 500 output tokens costs $0.00824 with MoonshotAI: Kimi K2.6 and $0.00875 with Relace: Relace Search.
In depth
MoonshotAI: Kimi K2.6 is a text model from Moonshot AI, released on 2026-04-20. Relace: Relace Search is a text model from Relace, released on 2025-12-08. Both are served on api14 under the text category, and both accept text input and return text output, so at the surface level they look like interchangeable chat endpoints. The differences show up once you look at what each one accepts and how much control it gives you.
Kimi K2.6 carries a context window of 262144 tokens and accepts both text and image input, while Relace Search has a context window of 256000 tokens and accepts text only. Those two context sizes are close enough that neither is meaningfully bigger for practical purposes, but the modality gap is real: if your request includes an image, Kimi K2.6 is the only one of the two that can take it. Kimi K2.6 also exposes a much wider parameter surface, including reasoning and include_reasoning, structured_outputs, logit_bias, logprobs, top_logprobs, min_p, top_k, repetition_penalty, frequency_penalty, presence_penalty, parallel_tool_calls and seed. Relace Search supports a narrower set: max_tokens, response_format, seed, stop, temperature, tool_choice, tools and top_p. Kimi K2.6 is the only one of the pair with structured outputs and the only one with reasoning controls, so tasks that depend on either of those belong on Kimi K2.6.
For day to day work, the split is fairly clean. If you need a model to reason through a problem step by step, emit a strict JSON shape via structured outputs, or handle a prompt that mixes text and images, call Kimi K2.6. If you are running straightforward text generation or tool calling where you only need temperature, top_p, stop sequences and a response format, Relace Search covers that with fewer knobs to tune. Cost is worth checking in the live price table before you commit, since Kimi K2.6 is the cheaper of the two on input while Relace Search is the cheaper on output. That means the cheaper choice depends on your input to output ratio: prompt heavy workloads lean toward Kimi K2.6, while generation heavy workloads lean toward Relace Search.
Calling either model through api14 works the same way because the API is OpenAI compatible. You point your existing client at the api14 base URL, send your messages to the chat completions endpoint, and set the model field to either "moonshotai/kimi-k2.6" or "relace/relace-search". Nothing else in the request shape changes. Switching between them is a one line edit to the model id, so you can benchmark both against your own traffic without rewriting your integration. Keep in mind that parameters are not universal: if you send reasoning or structured_outputs to Relace Search, or logprobs and logit_bias, expect them to be rejected or ignored, so gate those fields on the model you are calling.
There are still cases where Relace Search is the better pick even though Kimi K2.6 has the broader feature set. If your workload is output heavy and you do not need reasoning traces, structured outputs or image input, Relace Search is cheaper on output and its smaller parameter list is enough for plain text generation and tool calling. It is also the older of the two releases, so if you already have a working integration against it, there is no capability reason in these facts to move off it unless you specifically need the extra modalities or parameters that Kimi K2.6 provides.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "moonshotai/kimi-k2.6" to "relace/relace-search" and prompts, streaming and tools keep working.
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": "relace/relace-search",
"messages": [
{
"role": "user",
"content": "Explain vector databases in two sentences."
}
]
});
console.log(result.choices[0].message.content);
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