Kimi K3 vs GLM-5.2: The Two Open Flagships Compared

Kimi K3 and GLM-5.2 are the two flagship open models on AI Space, and they come at coding from different angles. Kimi K3 is Moonshot's newest and largest model, a 2.8-trillion-parameter reasoner with a 1M-token context and native vision. GLM-5.2 is Z.ai's agentic coding flagship, the model that sits at #1 for web and UI generation on DesignArena and has been AI Space's workhorse for long-horizon coding. If you're choosing a default, the question is whether you want K3's raw scale and context or GLM-5.2's proven strength on agentic, front-end-heavy work.

TL;DR

Kimi K3 is the more capable model on paper and the new default on AI Space: 2.8T parameters, a 1M-token context window, native vision, and reasoning you can dial per request. GLM-5.2 is the more established flagship, especially strong on agentic coding, multi-file edits, and web/UI generation, where it holds the top DesignArena spot. Lead with K3 when you want the longest context, vision input, or the strongest general output; lead with GLM-5.2 for front-end and UI work and long-horizon agentic coding where its track record is proven. Both run on AI Space through one OpenAI-compatible API, so you don't have to commit to one.

By the numbers

K3 edges GLM-5.2 on both shared benchmarks (GPQA Diamond 93.5 vs 91.2; HLE-with-tools 56.0 vs 54.7) but costs roughly 2× on input and 3.4× on output. K3 adds a 1M context and native vision; GLM-5.2 also runs a 1M context but is text-only. All scores are vendor-reported.

Kimi K3 overview

Kimi K3 is Moonshot AI's newest flagship and the largest model on AI Space: 2.8 trillion total parameters, served with a 1M-token context window. It uses Kimi Delta Attention, a hybrid linear-attention design with attention residuals, which is what makes that million-token window practical to serve rather than nominal. It's natively multimodal, taking image input alongside text, and its reasoning is always on with a reasoning_effort parameter you set per request (up to a "max" tier for the hardest problems), so a single model covers both quick answers and deep work depending on how much thinking you ask for.

For coding, K3 is the strongest model in the lineup by scale, architecture, and positioning. Moonshot hasn't published SWE-bench or LiveCodeBench numbers for it yet, so we won't quote one. Treat it as the top-capability open model on AI Space rather than a benchmark-confirmed figure. Where it clearly wins outright is modality: native vision opens up image-driven tasks that a text-only model can't match in one request.

GLM-5.2 overview

GLM-5.2, from Z.ai, is a flagship agentic coding model with a 1M-token context window. It's built for the full coding workload, function generation, refactoring, test writing, documentation, and the harder multi-file refactors, and it produces explicit failure signals instead of failing silently, which matters for automated pipelines. It's an extended-thinking model and handles multilingual codebases gracefully.

Its standout strength is front-end and UI generation: GLM-5.2 sits at #1 for web and UI on the DesignArena leaderboard, which is a real, independently ranked result rather than a self-reported number. For teams whose work skews toward building interfaces, that track record is the reason to reach for it. It's also the model many teams already have their agentic workflows tuned around, so its behavior on long-horizon coding runs is a known quantity.

Head-to-head

How to pick

Lead with Kimi K3 when you want maximum capability or the long context: hard refactors, dense logic, whole-repo analysis, agent runs that accumulate a lot of history, or anything with image input. Its 1M window and native vision are capabilities GLM-5.2 doesn't have, and as the newest, largest flagship it's the model to reach for when output quality is the priority.

Lead with GLM-5.2 when the work is front-end and UI generation (that's where its DesignArena result is directly relevant), or when you already have long-horizon agentic workflows tuned around it and value a known quantity. It's a strong, capable flagship with a 1M context that covers the large majority of coding tasks well.

For many teams the pragmatic setup is both: K3 as the default for the hardest work and anything needing long context or vision, GLM-5.2 for UI-heavy work and the agentic pipelines already built around it. Switching is a model-field change.

Running both on AI Space

Both flagships are available through AI Space's OpenAI-compatible API on one subscription. List them in your provider config and switch with the picker:

"models": {
  "kimi-k3": { "name": "Kimi K3" },
  "glm-5.2": { "name": "GLM-5.2" }
}

In code it's the model field: kimi-k3 or glm-5.2. Both run on Cloudflare's network across the US, UK, Germany, Japan, and Australia (inference on Western infrastructure, not Moonshot's or Z.ai's origin servers), and both bill at flat monthly rates (Starter $25, Pro $125) with per-user spend ceilings rather than open-ended per-token pricing.

If you want to see K3 against Moonshot's previous flagship, see Kimi K3 vs Kimi K2.7 Code, or against the proprietary frontier in Kimi K3 vs Claude Opus 4.8. For the whole lineup, see our roundup of the best open-source coding models in 2026. Get started with AI Space and run both on Cloudflare's global network.