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Kimi K2.7 Code vs Qwen3 235B A22B Thinking 2507: API price, context & features · api14

MoonshotAI: Kimi K2.7 Code vs Qwen: Qwen3 235B A22B Thinking 2507

A side-by-side comparison of MoonshotAI: Kimi K2.7 Code ($2.10 / 1M tokens) and Qwen: Qwen3 235B A22B Thinking 2507 ($1.58 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	MoonshotAI: Kimi K2.7 Code	Qwen: Qwen3 235B A22B Thinking 2507
Provider	Moonshot AI	Qwen
Input	$2.10 / 1M tokens	$1.58 / 1M tokens
Output	$12.25 / 1M tokens	$12.25 / 1M tokens
Cache read	$0.56 / 1M tokens	-
Context window	262,144	128,000
Max output	235,929	16,384
Input	text, image	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jun 12, 2026	Jul 25, 2025

Key differences

Qwen: Qwen3 235B A22B Thinking 2507 is 25% cheaper on input tokens.
MoonshotAI: Kimi K2.7 Code reads 2.0× more context (262,144 tokens).
Only MoonshotAI: Kimi K2.7 Code supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00823 with MoonshotAI: Kimi K2.7 Code and $0.00770 with Qwen: Qwen3 235B A22B Thinking 2507.

In depth

MoonshotAI: Kimi K2.7 Code is made by Moonshot AI, and Qwen: Qwen3 235B A22B Thinking 2507 is made by Qwen. Both are text models, meaning text is what they return, and both are served through api14 under the same OpenAI-compatible surface. The Kimi model was released by its provider on 2026-06-12, while the Qwen model was released on 2025-07-25. The naming tells you something about intent: Kimi K2.7 Code is positioned around code work, and the Qwen model carries the Thinking label, which points at reasoning-heavy generation.

The clearest split is context and input. Kimi K2.7 Code accepts text and image input and offers a context window of 262144 tokens. Qwen3 235B A22B Thinking 2507 accepts text only and offers 128000 tokens. So if your prompt includes a screenshot, a diagram, a scanned page or any other image alongside text, only the Kimi model can take it. If your task is pure text and fits comfortably inside 128000 tokens, both are viable. On parameters, Kimi K2.7 Code exposes a wider set, including structured_outputs, response_format, logit_bias, logprobs, top_logprobs, min_p, repetition_penalty, parallel_tool_calls and seed. The Qwen model supports tools, tool_choice, reasoning, include_reasoning, temperature, top_p, top_k, frequency_penalty, presence_penalty, max_tokens and stop. Both support tool calling, so neither has an exclusive advantage there, but structured output constraints are only listed for the Kimi model.

For task selection, start from what the request actually needs. Long repository context, multi-file refactors, large logs, or anything where you want the model constrained to a JSON schema or a specific response format points to Kimi K2.7 Code, because of the larger window and the structured output parameters. Work that is primarily reasoning over a moderate amount of text, where you want the model to think before answering and you do not need images or schema enforcement, is a reasonable fit for Qwen3 235B A22B Thinking 2507. If you are unsure, the deciding questions are simple: does the input contain an image, does the prompt exceed 128000 tokens, and do you need structured outputs? A yes to any of those rules out the Qwen model for that call.

Calling either one through api14 is the same operation. You send a request to the OpenAI-compatible chat completions endpoint with your api14 key, and the only thing that changes between the two models is the model id string. Use "moonshotai/kimi-k2.7-code" for the Kimi model and "qwen/qwen3-235b-a22b-thinking-2507" for the Qwen model. Messages, tools, temperature and the rest of the request body keep the same shape, so switching is a one-line edit and you can A/B the two on your own traffic without rewriting client code. Just remember to only send fields the chosen model lists as supported, since the Qwen model does not accept the structured output or logprob parameters.

There are cases where the Qwen model is still the better pick even though the Kimi model has the larger window and more parameters. Qwen3 235B A22B Thinking 2507 is the cheaper option on input, and output pricing is about the same between the two, so for high-volume text-only reasoning where input tokens dominate your bill, the Qwen model can cost less to run. It also supports reasoning and include_reasoning, so if your workflow depends on inspecting the model's thinking, that path is available there too. If your prompts are text-only, fit inside 128000 tokens, and you do not need structured outputs, the Qwen model is a legitimate default rather than a fallback.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "moonshotai/kimi-k2.7-code" to "qwen/qwen3-235b-a22b-thinking-2507" and prompts, streaming and tools keep working.

MoonshotAI: Kimi K2.7 Code details & playground →Qwen: Qwen3 235B A22B Thinking 2507 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": "qwen/qwen3-235b-a22b-thinking-2507",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

MoonshotAI: Kimi K2.7 Code vs Qwen: Qwen3 235B A22B Thinking 2507

Specifications

Key differences

In depth

Switch with one string

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