Kimi K3 vs GLM-5.2: Coding, Pricing & Agent Tests

AI Takeaway

• Which model is stronger overall? Kimi K3 scores 57 on the independent Artificial Analysis Intelligence Index, versus 51 for GLM-5.2. It also has the stronger early profile for difficult coding, research, and agent work.
• Which model offers better value? GLM-5.2. Fresh input costs $1.40 per million tokens and output costs $4.40, compared with K3 at $3 and $15. It is also much faster in current independent measurements.
• Which is better for coding agents? Start with K3 when the task is difficult, visual, or expensive to get wrong. Choose GLM-5.2 for repeated repo work, long tool loops, and cost-sensitive automation.
• Can you self-host either model today? GLM-5.2 weights are available under the MIT license. K3's weights are not yet downloadable; Moonshot says they will arrive by July 27, 2026, and the model's scale demands serious infrastructure.
• What is the practical verdict? K3 is the capability-first choice. GLM-5.2 is the speed, cost, and deployment choice. Test both on the same end-to-end task before choosing a default.

Kimi K3 vs GLM-5.2 at a Glance

Kimi K3 is the stronger first test when quality matters more than token spend. GLM-5.2 is easier to justify for high-volume work or controlled deployments. The GLM-5.2 model page provides a closer look at its specifications and agent fit.

Factor	Kimi K3	GLM-5.2	Practical meaning
Best first test	Difficult coding, visual work, research	High-volume coding and automation	Match the workload
Independent intelligence score	57	51	K3 has the stronger overall signal
API price per 1M tokens	$3 input / $0.30 cached / $15 output	$1.40 input / $0.26 cached / $4.40 output	GLM-5.2 is materially cheaper
Observed output speed	About 62 tokens/s	About 156 tokens/s	GLM-5.2 has a clear throughput edge
Context window	1M tokens	1M tokens	Context size does not decide this comparison
Input	Text and native vision	Text	K3 can work directly from images
Weights	Promised by July 27	Available under MIT	GLM-5.2 is usable today

What the Benchmarks Actually Show

The Independent Result Favors Kimi K3

Artificial Analysis gives K3 an Intelligence Index score of 57, compared with 51 for GLM-5.2. The index combines reasoning, knowledge, coding, and agent evaluations. It is a broad capability signal, not proof that K3 will win every repository or workflow.

Independent results still cannot reproduce your files, acceptance checks, tools, or retry policy. This recent Claude Sonnet 5 vs GLM-5.2 comparison provides another premium-model reference point.

K3 Leads Moonshot's Coding and Agent Results

Moonshot's launch results put K3 ahead of GLM-5.2 on several demanding tests. These are vendor-published comparisons, so the setup matters:

Benchmark	Kimi K3	GLM-5.2	What it signals
Terminal Bench 2.1	88.3	82.7	Terminal and tool execution
Program Bench	77.8	63.7	Larger implementation tasks
SWE Marathon	42.0	13.0	Sustained engineering work
GDPval-AA v2	1668	1514	Professional tasks
MCP Atlas	84.2	82.6	Agent tool use
Automation Bench	30.8	12.9	Multi-step SaaS automation

The largest gaps appear in sustained engineering and automation, where retries and context drift can ruin a promising run.

Why the Harness Can Change the Winner

Moonshot's table mixes KimiCode, Claude Code, Codex, and other harnesses. The runtime controls tool descriptions, context compression, error handling, and retry rules. A model can look stronger because its surrounding system helps it recover. Use the results to choose a serious test, not to declare a universal winner.

Price and Speed: GLM-5.2 Has the Production Advantage

API Cost in a Real Agent Loop

For one million fresh input tokens and 200,000 output tokens, published rates put the run at about $6 with K3 and $2.28 with GLM-5.2. K3's output price is roughly 3.4 times higher, so long reasoning and repeated tool traces add up quickly.

Caching narrows the gap for stable instructions and unchanged project context. It helps less when browser pages, logs, and tool state keep changing.

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Measure Cost per Completed Task

The rate card is only the first line of the bill:

Real task cost = model spend + retries + failed tool calls + review time + incorrect-action risk

K3 can earn back its premium by solving a difficult problem cleanly when a cheaper model needs several corrections. GLM-5.2 wins when work is repeatable, verifiable, and performed at volume. The guide to the best model for OpenClaw adds reliability, tool use, and workload fit to the price comparison.

Artificial Analysis currently measures about 62 output tokens per second on Kimi's API and 156 for GLM-5.2 across tracked providers. Because those provider pools differ, the ratio is not a controlled head-to-head result. Even so, faster iteration matters during test-fix-test loops.

Context, Vision, and Open Weights Change the Decision

Kimi K3 Is the Better Multimodal Creator

Both models support a one-million-token context window, but only K3 accepts images natively. It can rebuild a page from a screenshot, check a frontend against a reference, interpret documents, or iterate on game and CAD output. A coding agent can inspect what it produced instead of relying on text alone.

At launch, K3 defaults to maximum reasoning effort, so large visual or repository contexts can increase latency and cost. Use the full window when a task truly depends on distant information, not for every available file.

GLM-5.2 Is Easier to Deploy and Scale Today

GLM-5.2 is text-only, but its MIT-licensed weights are available now. Its mixture-of-experts design has 753 billion total parameters and activates roughly 40 billion at a time. Z.AI and multiple inference providers serve it.

Open weights do not turn a frontier model into a casual laptop install. K3 has 2.8 trillion parameters, and Moonshot recommends supernode configurations with at least 64 accelerators. As of July 17, its weights are not downloadable. GLM-5.2 is the practical choice for provider flexibility and data control; K3 wins when native vision outweighs infrastructure concerns.

Which Model Should You Choose?

Choose Kimi K3 for Hard, High-Value Work

Run K3 first for visual frontend iteration, complex research, multi-source synthesis, or long decision chains where an error costs more than extra tokens. It is also stronger when an agent must inspect screenshots and refine its output.

Choose GLM-5.2 for Volume, Speed, and Control

GLM-5.2 fits repeated repository maintenance, test-driven fixes, scheduled jobs, and cost-sensitive automation. Lower rates make retries easier to tolerate. Open weights and provider choice matter when deployment location or data control is required.

For a concrete workload, the coding agent use case covers pull-request reviews, debugging, testing, and repository maintenance.

Use Both When the Workload Changes

Route repeatable exploration and bulk execution to GLM-5.2, then escalate visual or judgment-heavy work to K3. A reusable coding-agent skill can keep instructions and reporting consistent. Switch at task boundaries with a clean brief; a mid-session switch can leave the second model with compressed context and unexplained decisions.

Run a Fair Kimi K3 vs GLM-5.2 Agent Test

Public benchmarks narrow the shortlist, but two chat tabs do not test agent performance. A fair comparison needs identical files, tools, permissions, limits, and a visible finish line. MyClaw provides a managed, always-on OpenClaw or Hermes Agent workspace with persistent files, tools, skills, browser workflows, and scheduled jobs. Check the live model catalog first, then compare supported models under matched conditions.

Step 1: Pick a Task Worth Finishing

Choose one observable job: fix a failing test, audit a repository, research 20 cited sources, rebuild a page from a screenshot, or create a competitor brief. Set three pass conditions and a time or spending limit. A task that cannot pass or fail only produces subjective impressions.

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Step 2: Keep the Workspace Fixed and the Sessions Clean

Give each model the same prompt, files, tools, permissions, and checks. Start fresh sessions. Record completion, retries, wrong tool calls, elapsed time, token cost, and corrections. If the brief improves after one run, rerun both models to keep the comparison fair.

Step 3: Turn the Winner Into a Working System

Choose the model with the lowest cost per accepted result. Save the instructions as a reusable skill or schedule the workflow. Keep the other model as an escalation path for harder jobs, visual tasks, or provider outages. Neither model must win every category to be useful.

Conclusion: Choose the Model That Finishes the Work

The Kimi K3 vs GLM-5.2 decision comes down to the workload. K3 is stronger overall and better equipped for multimodal, high-difficulty agent work. GLM-5.2 is faster, cheaper, open today, and easier to justify at production volume. Do not choose a permanent default from launch charts alone. Run the same real task, measure accepted results, supervision, speed, and total cost, then assign each model to the work it completes best.