Kimi K2.6 vs GLM-5.1: Benchmarks, Context Window, Pricing, and Which Model Fits Better

Kimi K2.6 vs GLM-5.1: Benchmarks, Context Window, Pricing, and Which Model Fits Better

In April 2026 two of the strongest open-weight models in the world came out of China within two weeks of each other: GLM-5.1 from Z.AI on April 7, and Kimi K2.6 from Moonshot AI on April 20. Both target long-horizon coding and autonomous agent workloads. Both claim frontier-level performance. Both are permissively licensed. But they are genuinely different models with different strengths, and the right pick depends on what you are building.

This comparison walks through architecture, benchmarks, multimodal support, context, pricing, and API experience — using each vendor's own published numbers — and ends with a direct recommendation by workflow.

Quick answer

• Pick Kimi K2.6 if you need native vision or video input, want the longest usable context (256K), integrate heavily with OpenAI-compatible SDKs, or run agent-swarm workloads with parallel sub-agents.
• Pick GLM-5.1 if you need extremely long single-output generation (128K max output), run ultra-long-horizon engineering tasks (Z.AI's own material describes up to 8-hour autonomous runs), or want a text-only model with the current top self-reported SWE-Bench Pro score and USD-denominated API pricing.
• Neither is strictly "better." Architecture, modality, and pricing differ enough that the honest answer depends on your workflow.

Release timing and positioning

	Kimi K2.6	GLM-5.1
Vendor	Moonshot AI	Z.AI (formerly Zhipu AI)
Release date	April 20, 2026	April 7, 2026
Positioning	Open-weight, multimodal, agentic coding + swarm	Open-weight, text-only, long-horizon engineering
License	Modified MIT	MIT

Both vendors pitch the same story: long-horizon autonomous coding by open-weight models built outside the US compute stack. The packaging differs — Kimi leans into multimodality and multi-agent orchestration, Z.AI leans into sustained single-task execution.

Model capability snapshot

	Kimi K2.6	GLM-5.1
Architecture	Mixture-of-Experts	Mixture-of-Experts
Total parameters	~1T	~754B
Active parameters	~32B	~40B
Context window	256K	200K
Max output	(uses context window)	128K
Text input	Yes	Yes
Image input	Yes (native, via MoonViT)	No
Video input	Yes (native, experimental on third-party engines)	No
Thinking mode	Yes	Yes
Function calling	Yes	Yes
MCP support	Yes	Yes
Structured output	Yes	Yes

The first-order difference jumps out of this table: Kimi K2.6 is multimodal, GLM-5.1 is text-only. Z.AI's own documentation and third-party reviewers consistently confirm GLM-5.1 does not accept image or audio input. If your product involves screenshots, design mockups, video analysis, or PDF pages as images, Kimi is the only one of the two that fits.

The second-order difference: context versus output length. Kimi offers a longer context (256K vs 200K), while GLM-5.1 emphasizes a larger max output window (128K). That matters for different tasks — loading an entire codebase favors Kimi's context; generating an extremely long single piece of code or documentation favors GLM's output budget.

Coding and agent benchmark claims

Both vendors publish official benchmark tables. These are self-reported on each vendor's preferred harness, so treat them as positioning rather than head-to-head leaderboard results.

SWE-Bench Pro (coding)

Model	Score (self-reported)
GLM-5.1	58.4
Kimi K2.6	58.6

Within margin of error. Both vendors claim to have topped GPT-5.4 and Claude Opus 4.6 on this benchmark — and on their own runs, they both do. A point of separation between them is not a meaningful real-world gap.

SWE-Bench Verified

Kimi K2.6 reports 80.2. GLM-5.1 is reported at roughly 77.8 on the same benchmark in earlier Z.AI materials. Kimi edges out on this one.

Terminal-Bench 2.0

Kimi K2.6 reports 66.7 on the Terminus-2 harness. GLM-5.1 reports 63.5 on Terminus-2 and up to 66.5 on the Claude Code harness. Again, within reasonable proximity — the harness matters as much as the model.

Agentic / browsing

• Kimi K2.6: BrowseComp 83.2, Toolathlon 50.0, HLE-with-tools 54.0.
• GLM-5.1: BrowseComp 68.0 (79.3 with context management), MCP-Atlas 71.8, τ³-Bench 70.6.

These are measuring different things under different harnesses, so direct comparison is genuinely misleading. The defensible summary: both models are competitive with closed-source frontier models on agentic coding tasks, with Kimi tilted toward tool-use-heavy benchmarks and GLM tilted toward long, iterative execution.

Long-horizon demos

Each vendor published an anchor demo with their release:

• Kimi K2.6: 12+ hours of continuous execution optimizing Qwen3.5-0.8B local inference in Zig, 4,000+ tool calls, ultimately ~20% faster than LM Studio; and a 13-hour autonomous refactor of exchange-core with roughly 185% medium-throughput gain.
• GLM-5.1: 8 hours of continuous autonomous work on a single engineering task, 655 iterations on a vector database optimization pipeline, boosting query throughput to 6.9× the initial production version.

Different flavors of the same pitch. Kimi emphasizes breadth (many languages, many domains, tool calls in parallel). GLM emphasizes depth (one task, one codebase, long iteration loop).

Multimodal difference

This is the cleanest, most actionable difference between the two models and it is worth isolating:

Input type	Kimi K2.6	GLM-5.1
Text	✅	✅
Image	✅	❌
Video	✅ (experimental on third-party engines)	❌

If your workflow includes any of the following, GLM-5.1 is not in the running and you should stop comparing:

• Converting design mockups or screenshots into working front-end code
• Analyzing PDFs visually (as opposed to text-extracted)
• Reading plots, diagrams, or figures inside documents
• Any video input task

If your workflow is pure text — source files, logs, documentation, natural-language prompts — modality is not a differentiator and the decision shifts to the pricing, context, and workflow-fit sections below.

API and integration experience

Both offer OpenAI-style APIs, but the developer experience diverges.

Kimi K2.6. Moonshot's API is fully OpenAI-compatible at https://api.moonshot.ai/v1. Any OpenAI SDK works as a drop-in client — change the base URL and the model string and you are done. Supports thinking and non-thinking modes, tool calling, structured output, and native multimodal content parts. Docs are in Chinese and English.

GLM-5.1. Z.AI's BigModel API also exposes an OpenAI-compatible surface, with support for thinking mode, function calling, MCP integration, structured output, and context caching. Rich engineering-agent tooling, with documented workflows for Claude Code and OpenClaw.

If you already have an OpenAI client pointing at production and you are just swapping models, both are easy to plug in. If you are migrating from Claude Code or a similar CLI coding agent, both vendors have explicit integration guides.

Pricing comparison

This is the area where casual "which is cheaper?" answers are most misleading, because the two vendors publish in different currencies and use different billing shapes.

Kimi K2.6 (official pricing, RMB)

Item	Price
Cached input	¥1.10 / 1M tokens
Uncached input	¥6.50 / 1M tokens
Output	¥27.00 / 1M tokens
Context window	262,144 tokens
Built-in web search	¥0.03 per call + result tokens

GLM-5.1 (Z.AI pricing, USD)

Z.AI's published GLM-5.1 rates have included:

Item	Price
Input	$1.40 / 1M tokens (some sources reference $1.00)
Cached input	$0.26 / 1M tokens
Output	$4.40 / 1M tokens (some sources reference $3.20)
Context window	200K

Z.AI also offers a GLM Coding Plan subscription separate from API billing, with monthly tiers aimed at individual coding-agent usage.

Reading the pricing honestly

There are three reasons a single "cheaper than" sentence is wrong here:

1. Different currencies. Kimi bills in RMB, Z.AI bills in USD. The naïve "¥6.50 vs $1.40 so GLM is cheaper" is not even close to valid without a current FX conversion.
2. Different cache economics. Kimi's cached-vs-uncached ratio is roughly 6×; GLM's is roughly 5–6×. In both cases, cache-friendly prompt design dominates the effective bill.
3. Different usage shapes. If your workload uses vision, Kimi is the only option — pricing comparison is moot. If your workload generates enormous outputs, GLM's output rate and 128K output ceiling matter more than Kimi's. If your workload is agent-swarm–style, Kimi's swarm features may save you more than GLM's cheaper per-token rate costs you.

The practical advice: list the official prices in the vendor's own currency, convert at today's rate for sanity, and then model your actual token mix (cached input / uncached input / output / tool calls / web search) before concluding anything about cost.

Which one should you choose

Choose Kimi K2.6 if:

• You need image or video input — this is the single cleanest deciding factor.
• You want the longest usable context window (256K) for whole-codebase or long-document work.
• You are building around multi-agent orchestration or plan to use Moonshot's Agent Swarm capabilities.
• You already run OpenAI SDK-based services and want the drop-in OpenAI-compatible endpoint.
• You need strong Chinese-language capability.
• You want to integrate tightly with Claude Code, Codex, OpenCode, OpenClaw, or Kimi Code.

Choose GLM-5.1 if:

• Your workload is pure text and you do not need any multimodality.
• You need very long single-response outputs — the 128K max output ceiling is meaningful.
• You are sensitive to the self-reported SWE-Bench Pro leaderboard position for marketing or procurement reasons.
• You need MIT-licensed weights (GLM-5.1's license is a clean MIT, while Kimi's is a Modified MIT with an attribution clause for very large deployments).
• You prefer USD-denominated API billing over RMB.
• Your workflow is one-task, one-agent, extremely long runs — the "8-hour autonomous Linux desktop build" and "655 iterations" shape of task.

Short recommendation by workflow

Workflow	Pick
Screenshot → working UI	Kimi K2.6
Autonomous multi-agent coding swarm	Kimi K2.6
Long single-task engineering optimization	GLM-5.1
Generating very long documents or code in one go	GLM-5.1
RAG over a 200K+ knowledge base	Kimi K2.6
Code review agent in VS Code / IDE	Either — pick by pricing fit
Chinese-first bilingual product	Kimi K2.6
Pure-text long-horizon agent without vision	Close call, test both

Final verdict

This is not a "winner" comparison. Both Kimi K2.6 and GLM-5.1 are legitimately frontier-class open-weight models, and the choice between them is best made on modality and workflow shape, not on a single-digit benchmark gap.

The cleanest filter: do you need vision or video input? If yes, Kimi K2.6 is your answer and GLM-5.1 is out of the running. If no, the decision becomes about output length, pricing currency and shape, and which vendor's integrations and docs you prefer. For many teams, the right move is to implement both behind an OpenAI-compatible client abstraction, run a week of production traffic, and let the actual cost-per-task numbers and reliability telemetry decide.

FAQ

Which is better for coding, Kimi K2.6 or GLM-5.1? On self-reported benchmarks they are within a point of each other on SWE-Bench Pro (58.6 vs 58.4). Kimi reports a higher SWE-Bench Verified (80.2). For pure text coding they are close; for any coding task that involves images (screenshots, diagrams, mockups), Kimi K2.6 wins by default because GLM-5.1 is text-only.

Does Kimi K2.6 support images and video? Yes. Kimi K2.6 is natively multimodal and accepts text, image, and video input. Video is marked experimental on third-party deployments.

Is GLM-5.1 multimodal? No. GLM-5.1 is text-only. For multimodal tasks, Z.AI publishes a separate GLM-5V-Turbo model; for straight GLM-5.1, image and video input are not supported.

Which model has the longer context window? Kimi K2.6 has a 256K context window. GLM-5.1 has a 200K context window. GLM-5.1's 128K max output is substantially longer than typical, which is a different lever from context length.

Which model is cheaper to use through the API? Neither has a clean "cheaper" answer because Kimi prices in RMB and GLM-5.1 in USD, and both use cache-tiered pricing where effective cost depends heavily on prompt design. Model your actual token mix per request before concluding.

Are both of these models open source? Yes, both publish weights on Hugging Face. GLM-5.1 uses the MIT license. Kimi K2.6 uses a Modified MIT license with a visible-attribution clause for very large deployments (roughly 100M+ MAU or $20M+ monthly revenue). For nearly all teams both are effectively permissive.