Short answer: Both Kimi K2.7 Code and GLM 5.2 are coding and reasoning models on EmpirioLabs with native web search and tool calling. Choose Kimi K2.7 Code for the lowest token price, multimodal input, and agentic coding with always-on reasoning. Choose GLM 5.2 for the largest 1M-token context window and reasoning effort you can dial up or down per request.
Kimi K2.7 Code vs GLM 5.2 at a glance
| Feature | Kimi K2.7 Code | GLM 5.2 |
|---|---|---|
| Maker | Moonshot AI | Z.ai |
| Context window | 256K tokens | 1M tokens |
| Reasoning | Always on | Adjustable effort |
| Inputs | Text, image, video | Text |
| Native web search | Yes, $0.015 per call | Yes, $0.033 per call |
| Function calling | Yes | Yes |
| Input price | $0.95 / 1M tokens | $1.40 / 1M tokens |
| Output price | $4.00 / 1M tokens | $4.40 / 1M tokens |
Context window: 256K vs 1M
GLM 5.2 has a 1M-token context window and up to 128K tokens of output, so it can hold a very large codebase, a long document, or an extended multi-file task in one request. Kimi K2.7 Code has a 256K context, which is still large and covers most coding sessions, but if you routinely feed in huge repositories or long transcripts, GLM 5.2 has the headroom.
Inputs and reasoning
Kimi K2.7 Code accepts text, image, and video input, so it can reason over a screenshot, a diagram, or a short clip alongside your code. It runs with reasoning always on and is tuned for agentic coding: generation, debugging, tool use, and long multi-step tasks. GLM 5.2 is text in and text out, but it lets you adjust reasoning effort per request, so you can spend more compute on a hard problem or keep it light and fast on a simple one.
Pricing: a typical coding request
Both bill per token, pay as you go. For a request with 100K tokens of input and 10K tokens of output:
- Kimi K2.7 Code: $0.135 (100K x $0.95 plus 10K x $4.00, per 1M)
- GLM 5.2: $0.184 (100K x $1.40 plus 10K x $4.40, per 1M)
Kimi K2.7 Code is the cheaper option on both input and output, and its native web search costs less per call ($0.015 versus $0.033). GLM 5.2 costs more, and in return gives you the 1M context window and adjustable reasoning effort.
When to use Kimi K2.7 Code
- You want the lowest token price for coding and reasoning.
- You need to send image or video input alongside text.
- You want agentic coding with always-on reasoning and cheaper built-in web search.
When to use GLM 5.2
- You need the largest context window, up to 1M tokens, for big codebases or long documents.
- You want to control reasoning effort per request.
- You want a long output budget, up to 128K tokens.
How to call either model
Both serve the OpenAI-compatible Chat Completions API, so switching is a one-line change. Point base_url at https://api.empiriolabs.ai/v1 and set the model id to kimi-k2-7-code or glm-5-2. Both support tool calling and native web search. You can also try them side by side in the playground.
Frequently asked questions
Which has the bigger context window?
GLM 5.2, with 1M tokens. Kimi K2.7 Code has a 256K context window.
Which is cheaper?
Kimi K2.7 Code: $0.95 input and $4.00 output per 1M tokens, versus $1.40 and $4.40 for GLM 5.2. Its native web search is also cheaper, at $0.015 per call versus $0.033.
Which supports image or video input?
Kimi K2.7 Code accepts text, image, and video. GLM 5.2 is text in, text out.
Can I control reasoning depth?
GLM 5.2 has adjustable reasoning effort per request. Kimi K2.7 Code runs with reasoning always on.
Are both OpenAI-compatible?
Yes. Both serve the OpenAI Chat Completions API. Point base_url at https://api.empiriolabs.ai/v1, set the model id, and existing OpenAI SDKs work unchanged.
