Kimi K3 vs GLM 5.2 vs DeepSeek V4 Pro

Kimi K3, GLM 5.2, and DeepSeek V4 Pro are three flagship reasoning models on EmpirioLabs, each with a 1M token context. All three reason before answering, call tools with function calling, and return structured JSON, and all three run on the same OpenAI-compatible API, so you can switch between them by changing one field. This post compares them on the facts that matter for real reasoning, coding, and agent work: context window, price, modalities, and capabilities.

At a glance

Input and output prices are current pay-as-you-go rates in USD per 1M tokens and can change. The live figures are always on each model page and the pricing page. All three also support reasoning and function calling with parallel tool calls, and all three can use web search for a small per-search fee.

Kimi K3

Moonshot's flagship reasoning model and the only one of the three that reads images and video alongside its 1M token context. Thinking is always on, and you can tune how hard it reasons with a reasoning effort control. It has native web search built in, so it fits multimodal analysis and agent loops that need live information as well as heavy reasoning. It is the most expensive of the three, so choose it when you need vision, top-tier reasoning, and a huge context in one model. See the Kimi K3 model page.

GLM 5.2

Z.ai's flagship reasoning and coding model, also 1M context, with adjustable reasoning effort and native web search. It has the lowest input price of the three and is text only, and its structured output is JSON mode, meaning any valid JSON object, rather than strict JSON schema. It is a strong pick for cost-sensitive text reasoning and coding that still wants live web results. See the GLM 5.2 model page.

DeepSeek V4 Pro

A strong reasoning and coding model at 1M context, priced between the other two. It is text only and returns strict JSON schema, which suits extraction and tool pipelines that need guaranteed structure. Web search is available as an optional add-on. Choose it for large text codebases and documents where strict structured output matters and multimodal input is not needed. See the DeepSeek V4 Pro model page.

Which one to pick

• Vision and top reasoning at scale: Kimi K3. The only one that reads images and video, with always-on tunable reasoning, native web search, and a 1M context.
• Lowest cost text reasoning: GLM 5.2. The cheapest input of the three, with adjustable reasoning effort and native web search.
• Strict JSON at a lower price: DeepSeek V4 Pro. JSON schema output and strong coding at 1M context when text only is fine.

How to call them

All three use the same endpoint. Swap the model field to switch:

curl https://api.empiriolabs.ai/v1/chat/completions \
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [{"role": "user", "content": "Explain and fix the bug in this function."}]
  }'

Use glm-5-2 or deepseek-v4-pro as the model id for the other two. Each also works on /v1/responses, /v1/messages, and the Gemini-compatible route.

Frequently asked questions

Which is the cheapest?

GLM 5.2 has the lowest input price of the three. DeepSeek V4 Pro sits in the middle, and Kimi K3 is the most expensive but is the only one with image and video input.

Do they all have a 1M context window?

Yes. Kimi K3, GLM 5.2, and DeepSeek V4 Pro all offer a 1M token context.

Which support image and video input?

Only Kimi K3. GLM 5.2 and DeepSeek V4 Pro are text only.

Which return strict JSON schema?

Kimi K3 and DeepSeek V4 Pro can force output that matches a schema you provide. GLM 5.2 supports JSON mode, which returns any valid JSON object.

Do they all support reasoning and tools?

Yes. All three reason before answering, do function calling with parallel tool calls, and can use web search.

Try any of them in the playground, or read the API docs.