Kimi K2.6 vs GLM 5.1: The Agent Model Math
Kimi is cheaper, multimodal, and scores higher on coding benchmarks. GLM is nearly 3x faster. For computer-use agents, the choice isn't obvious.
I moved to GLM 5.1 via Ollama Cloud because Anthropic banned third-party coding agents on subscription plans. If you pay per API token, you're fine. If you access Claude through a third-party harness like Pi Coding Agent on a Pro or Max subscription, you're cut off. My local hardware doesn't have the GPU memory to run frontier models at usable speed, so I need cloud inference either way. That's the kind of policy change that keeps you up at night when your entire workflow depends on a single provider.
Now Moonshot AI ships Kimi K2.6, and the comparison numbers make me pause. It's cheaper. It takes screenshots. It scores higher on coding benchmarks. On paper, it's the model I should be running.
But I'm not switching yet. Here's why.
The Headline Numbers
Let's get the benchmark table out of the way. According to OpenRouter's comparison data:
Kimi K2.6 costs $0.60 per million input tokens and $2.80 per million output tokens. It scores 53.9 on intelligence (99th percentile), 47.1 on coding (97th percentile), and 66.0 on agentic tasks (98th percentile). Context window is 262K tokens. It accepts both text and images. Max output is also 262K tokens.
GLM 5.1 costs $1.05 per million input tokens and $3.50 per million output tokens. It scores 51.4 on intelligence (97th percentile), 43.4 on coding (94th percentile), and 67.0 on agentic tasks (99th percentile). Context window is 203K tokens. Text only. Max output is 66K tokens.
Kimi wins on price by a wide margin: roughly 43% cheaper on input, 20% cheaper on output. It wins on coding benchmarks. It wins on context window size. Its output ceiling is four times what GLM can do in a single response.
GLM's only numerical wins are a marginally better agentic score (67.0 vs 66.0, which is basically noise) and more provider availability: 14 providers on OpenRouter versus Kimi's 5.
By the numbers alone, this looks like an easy call. Kimi is the better model at the better price.
The Number That Isn't on the Benchmark Table
But there's one number that matters more for computer-use agents than any benchmark score: throughput.
GLM 5.1 delivers 29.0 tokens per second at the p50. Kimi K2.6 delivers 11.0 tokens per second. That's not a small difference. That's nearly a 3x gap.
Why does this matter so much? Because computer-use agents don't write essays. They iterate. Fast. A typical computer-use loop looks like this: the agent takes a screenshot, decides what to do, generates a tool call, waits for the result, then does it again. And again. Dozens or hundreds of times per task.
When your agent is firing off small tool calls in rapid succession, throughput is what separates a workflow that feels responsive from one that feels broken. At 11 tokens per second, even a short response takes a noticeable beat. At 29 tokens per second, it's basically instant. Do that a hundred times and you're staring at the difference between a five-minute task and a fifteen-minute task.
For coding specifically — where I spend most of my agent time — the gap is even more pronounced. When I'm prompting an agent to modify a file, I want the response fast so I can read it, verify it, and move on. Waiting an extra few seconds per response adds up over a session.
The Multimodal Question
Kimi has one real edge: it takes images. GLM 5.1 is text-only.
For computer-use agents, that matters. Right now, my GLM-based agent reads the DOM tree and accessibility tree to figure out what's on screen. It works, mostly. But ARIA labels lie. Dynamic content hides. Visual layout — spacing, color, what's actually visible — is just missing.
Kimi can look at the actual screenshot. It sees what I see. For anything involving visual reasoning — checking if a UI looks right, reading charts, navigating interfaces without clean DOM structures — that's a gap GLM can't close.
The question is how often that matters in practice. For pure coding tasks where I'm editing files in a terminal or IDE, screenshots don't help much. For full-stack work where I'm testing web interfaces, debugging visual regressions, or interacting with design tools, screenshots change everything.
I suspect the ideal setup isn't choosing one model. It's routing: GLM for fast text-based coding iterations, Kimi for visual tasks that need actual eyes.
The Context and Output Limits
Kimi's max output is 262K tokens. GLM's is 66K. For most coding tasks, 66K is plenty. A file edit or function implementation is a few hundred tokens.
But agentic workflows run long. A code review across multiple files. A refactoring touching a dozen modules. Documentation generation. When the agent gets ambitious on you, 66K runs out. The response gets truncated — usually right where the summary or the final files live.
I've hit this with GLM. It's annoying. You re-prompt with "continue from where you left off" and hope the model picks up the thread. Kimi's 262K ceiling removes that problem.
Input context is less of an issue for me. Kimi's 262K versus GLM's 203K — most of my tasks don't come close to either. But for long-horizon tasks with a big working memory, the extra 60K tokens of headroom could matter.
Price vs. Speed: The Real Trade-Off
Kimi is cheaper per token. But if GLM's higher throughput means fewer total tokens per task — because the agent iterates faster and produces tighter outputs — the total cost gap might shrink or even flip.
I don't have enough runtime hours on Kimi to prove this. But slower models tend to be wordier. More reasoning tokens, more hedging, more circling before they commit. At 11 tokens per second, that extra text costs you on both speed and price.
Then there's the mental cost. Working with a slow agent is draining. You tab away, come back, find it still thinking, tab away again. Flow state breaks. For creative work, that friction hurts more than the API bill.
Provider Risk
GLM has 14 providers on OpenRouter. Kimi has 5. That's not about price shopping. It's about not getting stranded.
I learned this when Anthropic banned my use case. Multiple providers means if one cuts you off, suspends service, or goes down, you route to another. It's the same redundancy you want from any infrastructure dependency.
After Anthropic cut me off with a policy change, provider risk is part of every decision I make.
What I'm Actually Going to Do
I'm moving to Kimi K2.6.
GLM's speed is impressive, but I've been working with it long enough to notice the quality gap. Kimi scores higher on coding benchmarks, takes screenshots, and has a 262K output ceiling that means no more truncated responses. The 3x throughput difference is real, but I'd rather wait a few extra seconds per response and get better output than iterate faster on weaker reasoning.
Quality over speed. For the tasks that matter — complex debugging, multi-file refactors, architecture decisions — I want the model that gets it right more often, not the one that gets it wrong faster.
GLM stays as a fallback, and as the fast option for simple, deterministic tasks where throughput actually matters: lint fixes, formatting, boilerplate generation. But Kimi is the primary now.
If Moonshot improves Kimi's inference speed — or a provider offers a faster implementation than the current 11 tok/s — the math changes. The gap isn't model quality. It's infrastructure. Kimi is the smarter model. GLM is the faster pipe. Right now, I need both.
What Actually Matters
Benchmark scores make for good launch tweets. They don't tell you which model to run.
For computer-use agents, throughput is a constraint that matters as much as accuracy. A model that scores 5 points higher on a coding benchmark but takes 3x as long to respond is worse for iterative work. The benchmark that counts isn't SWE-bench. It's wall-clock time per agent loop.
Kimi K2.6 wins in the spec sheet. Cheaper, multimodal, bigger outputs, higher coding scores. GLM 5.1's 29 tokens per second is what makes agentic coding feel responsive. But responsive and right beats responsive and wrong.
I'll take quality over speed. Kimi is my primary now.
I run a multi-agent development workflow with Pi Coding Agent: a coding agent, a review bot, and a personal assistant. After Anthropic banned third-party coding agents on subscription plans, I moved to GLM-5.1 via Ollama Cloud — my local hardware can't run frontier models, so cloud inference is the only option. Now moving to Kimi K2.6 as primary, keeping GLM for fast simple tasks. Quality over speed.