What Is GLM-4.6? Zhipu AI's Agentic Coding Model, Explained

CodexFuse Team · 2026-11-06T12:12:23.829Z · 18 min read min read

What Is GLM-4.6? Zhipu AI's Agentic Coding Model, Explained

If you keep hearing the name GLM-4.6 in coding-model coverage and you want a straight answer to what is glm-4.6, here it is in one sentence: GLM-4.6 is Z.ai's open-weight flagship language model, built for agentic coding, reasoning, and long-context work. Z.ai is the international brand of Zhipu AI, the Tsinghua spin-out behind the GLM family, and GLM-4.6 was released on 30 September 2025, which makes it 402 days old on 6 November 2026. All facts in this guide are as of November 2026, and the model world moves fast, so treat prices and lineup claims as a snapshot, not a promise.

The word to understand first is agentic, because GLM-4.6 was designed to act, not just to answer. Our agentic AI explainer covers the idea in full, and in short it means a model that plans multi-step work, calls tools, and checks its own results.

GLM-4.6 does that with a thinking mode that reasons before it acts and with native tool calling built into the model itself. It is open-weight software under the MIT license, which means anyone can download the weights, run them, and build commercial products on top of them.

What Is GLM-4.6?

GLM-4.6 is a mixture-of-experts language model from Z.ai (formerly Zhipu AI) that turns a coding prompt into working software by reasoning, calling tools, and iterating on its own output. It ships as open weights on Hugging Face, runs through the Z.ai API or any OpenAI-compatible endpoint, and plugs into coding agents such as Claude Code, Cline, Roo Code, and Kilo Code. Its headline numbers are a 200,000-token context window and about 355 billion total parameters, with only 32 billion active for any single token.

That last detail is what makes the price possible. A mixture-of-experts model keeps many specialist sub-networks and wakes only a few of them per token, so GLM-4.6 behaves like a very large model while paying the compute bill of a much smaller one.

The result is a coding model that costs a fraction of the closed flagships while staying close to them on the benchmarks that matter for real agent work. The rest of this guide tests that claim honestly, with the maker's own numbers labeled as the maker's numbers.

How Big Is GLM-4.6? The Specs in Plain English

GLM-4.6 has about 355 billion total parameters, with 32 billion active per token, which is roughly 9.0 percent of the model doing the work at any moment. Artificial Analysis and the Hugging Face listing say 357 billion instead of 355 billion, so treat the total as about 355 billion with that small disagreement noted. The model reads up to 200,000 tokens of input, which is enough for a multi-file codebase review or a long agent trace, and it can write up to 128,000 tokens in a single answer.

Some aggregators list a 131,000-token maximum output, but the official figure is 128,000 tokens, so this guide uses the official number.

Its knowledge stops at 31 March 2025, so it does not know about events after that date unless a tool fetches them. It thinks in two gears: a thinking mode that works through hard problems step by step and calls tools while it reasons, and a fast non-thinking mode for direct answers. The license is MIT, the weights are free to download from Hugging Face under zai-org/GLM-4.6, and a smaller FP8 quantized version exists for cheaper serving.

One distinction saves confusion later. GLM-4.6 is the text model described here, while GLM-4.6V is a separate vision-language sibling released in December 2025, with 106-billion and 9-billion parameter variants and a 128,000-token context. They share a version number and a family name, but they are different models for different jobs.

What Changed From GLM-4.5 to GLM-4.6?

GLM-4.6 is an upgrade, not a redesign: it keeps the same 355-billion-parameter architecture as GLM-4.5 and changes what the model can do with it. The two releases are 64 days apart, with GLM-4.5 landing on 28 July 2025 and GLM-4.6 on 30 September 2025. The table below shows the changes Z.ai itself lists.

The token saving comes from Z.ai's own CC-Bench evaluation, where GLM-4.6 used about 14.7 percent fewer tokens per task than GLM-4.5, and the company's docs page quotes a larger figure, over 30 percent, on a different 74-test Claude Code set. Those are two different test sets, so the numbers do not combine into one claim. The headline change is the context window: 200,000 tokens is 72,000 more than the 128,000 GLM-4.5 offered, a 56.2 percent increase that matters most for long agent sessions.

What Does "Agentic" Actually Mean for GLM-4.6?

An agentic model does not stop at writing an answer; it plans the work, picks the tools, runs them, and checks the result. GLM-4.6 was built for that loop: its thinking mode reasons through a task, and it can decide on its own to call a web search, a calculator, or a code execution tool while it is still thinking. Native function calling with structured JSON output is built into the model, so the tools it calls return clean, machine-readable results.

One thing it does not do is host MCP natively. MCP is a separate open standard from Anthropic for connecting models to outside data and services, and our guide to what MCP is explains the protocol in full. GLM-4.6 can still work inside MCP-based agent systems through community servers such as CCGLM-MCP, which routes Claude Code through Zhipu's API, but the model itself is not an MCP host.

A number that sometimes gets attached to GLM-4.6 belongs to its predecessor. The 90.6 percent function-calling success rate quoted in some coverage is GLM-4.5's figure, not GLM-4.6's, and Z.ai has not published an equivalent headline number for 4.6. Treat any source that gives the 90.6 percent figure to GLM-4.6 as mistaken.

GLM-4.6 Benchmarks: How Good Is It at Coding?

The headline benchmark is CC-Bench, where Z.ai reports a 48.6 percent win rate for GLM-4.6 against Claude Sonnet 4 in multi-turn, real-world coding tasks judged by humans. That is near parity with one of the best coding models of 2025, but CC-Bench is Z.ai's own vendor-run evaluation, not an independent test, so read it as the maker's best case rather than a neutral verdict.

The independent picture is more mixed. DeepInfra, citing Artificial Analysis, reports an Intelligence Index of 56 for GLM-4.6 against 66 for DeepSeek-V3.2, a 10-point gap, but those figures come from an older version of the index, and Artificial Analysis has since moved to version 5.0.1, which no longer shows the same numbers on its live page. On SWE-bench Verified, the secondary figures quoted by cirra.ai put GLM-4.6 at 68.0 percent against 77.2 percent for Claude Sonnet 4.5, so the newest Claude still leads on that test.

Two warnings keep these numbers honest. First, different evaluation harnesses give very different scores: one secondary analysis quotes AIME at 93.9 percent and LiveCodeBench at 82.8 percent for GLM-4.6, far above the Artificial Analysis table's 44.3 percent and 56.1 percent, because the harnesses and settings differ. Never compare a number from one harness with a number from another.

Second, Z.ai's own announcement is candid about one limit: GLM-4.6 still lags behind Claude Sonnet 4.5 in coding ability, even while it competes with Sonnet 4.

GLM-4.6 vs Claude Sonnet: What the Numbers Say

The comparison most developers actually ask about is GLM-4.6 versus Claude Sonnet, because that is the choice between an open-weight model and Anthropic's closed flagship. On CC-Bench the two are near equals, with GLM-4.6 winning 48.6 percent of the head-to-head coding tasks. On SWE-bench Verified the newer Sonnet 4.5 pulls ahead, so the honest summary is that GLM-4.6 matches Sonnet 4 and trails Sonnet 4.5.

Price is where the gap opens up. Z.ai charges $0.60 per million input tokens and $2.20 per million output tokens, while the figures quoted by cirra.ai put Claude at $3 and $15 for the same million tokens. A job that sends one million input tokens and receives one million output tokens costs $2.80 with GLM-4.6 and $18.00 with Claude, which makes GLM-4.6 about 84.4 percent cheaper for that mix.

Claude's input price is 5.0 times GLM-4.6's and its output price is 6.8 times higher.

If you want the full picture of the Anthropic side, our guide to what Claude Code is explains the agent that made Sonnet famous. The choice is not really about which model is smarter in the abstract; it is about whether you want the cheapest capable coding model you can run yourself, or the strongest one you can only rent. GLM-4.6 wins the first question, and Sonnet 4.5 still wins the second.

How Much Does GLM-4.6 Cost?

GLM-4.6 pricing depends on the provider, and the first-party rate from Z.ai is $0.60 per million input tokens and $2.20 per million output tokens, verified on 6 November 2026. Cheaper providers sell the same model for less: DeepInfra lists $0.43 in and $1.75 out per million tokens on OpenRouter, which is 28.3 percent cheaper on input and 20.5 percent cheaper on output. A million tokens in and a million out costs $2.80 at Z.ai's rate and $2.18 at DeepInfra's, a saving of $0.62, or 22.1 percent.

Prices move often, so date-stamp anything you quote. OpenRouter-tracked changes logged moves on 9, 19, and 29 October 2026, and the claim of a change on 31 October could not be verified this run. There is no per-token price for running the weights yourself: self-hosting costs whatever your GPUs cost to rent, and no GLM-4.6-specific rental price was found this run.

How Do Developers Actually Use GLM-4.6?

Using GLM-4.6 takes seven steps from first try to daily driver, and most developers stop at step four. The list below follows the official docs and the routes the community actually uses.

1. Try it in the chat window at chat.z.ai and pick GLM-4.6, so you can feel the thinking mode before you spend anything.
2. Create an API key on the Z.ai API platform, which is where the pay-per-token billing lives.
3. Call the OpenAI-compatible API with your key, using the official Python SDK, the Java SDK, or plain cURL from the quick-start docs.
4. Point Claude Code at it by changing the model name to "glm-4.6" in your settings, or route it through Novita by setting ANTHROPIC_BASE_URL to https://api.novita.ai/anthropic and ANTHROPIC_MODEL to zai-org/glm-4.6.
5. Open your real repository in Claude Code, Cline, Roo Code, or Kilo Code, and give the model a multi-file task instead of a single snippet.
6. Let it use tools: the model's native function calling handles structured calls, and the community CCGLM-MCP server connects it to MCP-based agent systems.
7. Self-host when the volume justifies it, by serving the Hugging Face weights with vLLM or SGLang instead of paying per token.

Step four is where the money changes hands, so it deserves the most attention. Our Claude Code beginner guide walks through the agent itself, and the Novita route is the one the community documents most, because it needs only two environment variables and no code changes. The GLM Coding Plan is the other route: subscribers were auto-upgraded to GLM-4.6, and Z.ai markets the plan as a fraction of Claude's price with a larger usage quota, which is the company's own marketing claim rather than an independent finding.

# GLM-4.6 by the numbers - computed 6 November 2026
from datetime import date
release = date(2025, 9, 30)
today = date(2026, 11, 6)
print("Days since release:", (today - release).days)  # 402
print("Context increase %:", round((200 - 128) / 128 * 100, 1))  # 56.2
print("Active parameters %:", round(32 / 355 * 100, 1))  # 9.0
print("CC-Bench token saving %:", round((763 - 651) / 763 * 100, 1))  # 14.7
print("Z.ai 1M in + 1M out $:", 0.60 + 2.20)  # 2.8
print("DeepInfra 1M in + 1M out $:", 0.43 + 1.75)  # 2.18
print("Saving %:", round((2.80 - 2.18) / 2.80 * 100, 1))  # 22.1
print("Claude 1M in + 1M out $:", 3 + 15)  # 18
print("GLM vs Claude saving %:", round((18 - 2.80) / 18 * 100, 1))  # 84.4

Can You Run GLM-4.6 on Your Own Hardware?

Yes, and that is one of the main reasons developers choose it. The weights are published on Hugging Face as zai-org/GLM-4.6, with 26.3 thousand downloads recorded on the listing, and an FP8 quantized variant exists for cheaper serving. The official blog names vLLM and SGLang as the serving frameworks, and the SGLang cookbook adds EAGLE speculative decoding plus a thinking-budget flag for controlling how long the model reasons.

One thing to know before you go looking for the code: there is no dedicated GLM-4.6 repository on GitHub, because the inference code lives in the shared zai-org/GLM-4.5 repository. That is why no star count is quoted anywhere in this guide; none was published for a 4.6-specific repo. If you would rather run a coding agent on rented hardware than on your own machine, our guide to run Claude Code on a VPS covers that route.

Self-hosting is not free, it is just a different bill. You pay for the GPUs instead of the tokens, and the honest comparison is your monthly GPU rental against your monthly token spend at $0.60 and $2.20 per million. Heavy, steady workloads usually favor self-hosting, while bursty or light use usually favors the API.

What Came After GLM-4.6?

GLM-4.6 is a prior-generation model now, and saying so plainly is part of the honest picture. Z.ai shipped GLM-4.7 on 22 December 2025, only 83 days after GLM-4.6, then GLM-5 on 12 February 2026, 135 days after, and GLM-5.3 on 14 August 2026, 318 days after. As of 1 November 2026 there is no GLM-5.4 and no GLM-6, so GLM-5.3 remains the newest flagship.

The newer models do not make GLM-4.6 useless. It is still the cheapest MIT-licensed model in the family with a 200,000-token window, it is still served by most major providers, and it still powers the earliest tiers of Z.ai's GLM Coding Plan. What changed is the headline: GLM-4.6 is the 2025 open-weight coding model that still earns its keep, not Z.ai's current flagship.

GLM-4.6 vs GLM-4.5 vs DeepSeek-V3.2: Which One Fits Your Job?

Three open models cover most of the cheap-coding ground, and the right one depends on the job. GLM-4.6 wins on long context and agent stability, DeepSeek-V3.2 wins on throughput and cost, and GLM-4.5 is the older sibling that 4.6 replaces in almost every case. The table below lines them up.

DeepSeek-V3.2's context deserves one honest footnote. DeepInfra's own blog puts it at about 128,000 tokens, while OpenRouter lists about 164,000 for the same model, so the true number depends on the provider's configuration.

Throughput tells the same story as price: DeepInfra's blog reports about 14 tokens per second for DeepSeek-V3.2 against about 22 for GLM-4.6 on its platform, measured through OpenRouter. Pick DeepSeek when tokens are the bottleneck, and pick GLM-4.6 when the task needs a long memory and steady agent behavior.

Should You Use GLM-4.6 Today?

Use GLM-4.6 if you want a capable coding model you can run yourself, pay little for, and use commercially under the MIT license. It is the right pick for budget coding-agent setups, for self-hosted teams, and for anyone whose work lives in long files and long agent sessions. It is also the right pick when the price of the closed flagships is the thing stopping you from using an agent at all.

Skip it if you need the strongest coding model available, because Claude Sonnet 4.5 still leads on SWE-bench Verified and Z.ai's own announcement admits GLM-4.6 trails it. Skip it too if you need the newest Z.ai flagship, because that is GLM-5.3 now. And remember that CodexFuse offers no GLM-4.6 tool; this article is an explainer only, and the model itself lives on Z.ai's platform, on OpenRouter, and on your own hardware.

Sources

Z.ai, GLM-4.6: Advanced Agentic, Reasoning and Coding Capabilities, z.ai/blog/glm-4.6.

Z.ai Docs, GLM-4.6, docs.z.ai/guides/llm/glm-4.6.

Hugging Face, zai-org/GLM-4.6, huggingface.co/zai-org/GLM-4.6.

cirra.ai, GLM-4.6 Tool Calling and MCP Use: A Technical Analysis, cirra.ai/articles/glm-4-6-tool-calling-mcp-analysis.

cirra.ai, GLM-4.6 vs Claude Sonnet: A Performance and Cost Analysis, cirra.ai/articles/glm-4-6-vs-claude-sonnet-comparison.

Artificial Analysis, GLM-4.6 Reasoning, artificialanalysis.ai/models/glm-4-6-reasoning.

DeepInfra, GLM-4.6 vs DeepSeek-V3.2, deepinfra.com/blog/glm-4-6-vs-deepseek-v3-2-performance-deepinfra.

SGLang, GLM-4.6 Cookbook, docs.sglang.io/cookbook/autoregressive/GLM/GLM-4.6.

OpenRouter, z-ai/glm-4.6, openrouter.ai/z-ai/glm-4.6.

Requesty, Z.ai GLM-4.6, requesty.ai/models/zai/glm-4.6.

Puter, z-ai/glm-4.6, developer.puter.com/ai/z-ai/glm-4.6.

Layer3 Labs, GLM 4.6 Explained, layer3labs.io/guides/glm-4-6-explained.

MarkTechPost, Zhipu AI Releases GLM-4.6, marktechpost.com, 30 September 2025.

Wikipedia, Z.ai, en.wikipedia.org/wiki/Z.ai.

Second Talent, Every GLM AI Model Explained, secondtalent.com.

VersusApp, GLM-5.4 Release Date, versusapp.com.

A2Agent, GLM-6 Release Date, a2agent.me.

BenchGecko, GLM 4.6 Benchmarks, Pricing and Performance, benchgecko.ai/model/glm-4-6.

Frequently Asked Questions

Is GLM-4.6 free to use?

The weights are free to download under the MIT license, so running GLM-4.6 on your own hardware costs nothing in license fees. The hosted API is pay-per-token at $0.60 per million input tokens and $2.20 per million output tokens on Z.ai's first-party rate, verified on 6 November 2026. A free playground exists through Puter.js, which needs no API key for a quick try.

Is GLM-4.6 better than Claude Sonnet 4?

On Z.ai's own CC-Bench evaluation the two are near equals, with GLM-4.6 winning 48.6 percent of the head-to-head coding tasks. On the independent SWE-bench Verified test, the newer Claude Sonnet 4.5 still leads at 77.2 percent against GLM-4.6's 68.0 percent. The fair answer is that GLM-4.6 matches Sonnet 4, trails Sonnet 4.5, and costs about 84.4 percent less for the same tokens.

Can GLM-4.6 run locally?

Yes, the open weights are published on Hugging Face as zai-org/GLM-4.6, with an FP8 variant for cheaper serving. The official serving frameworks are vLLM and SGLang, and the SGLang cookbook documents EAGLE speculative decoding and a thinking-budget flag. You will need serious GPU hardware for a 355-billion-parameter model, so most individuals start with the API and self-host only when volume justifies it.

What is the GLM-4.6 context window?

GLM-4.6 accepts up to 200,000 tokens of input, up from 128,000 tokens in GLM-4.5, which is a 56.2 percent increase. It can write up to 128,000 tokens in a single answer. Some providers round the input window to 204,800 or 205,000 tokens, which is the same window expressed differently.

Is GLM-4.6 still worth using in 2026?

Yes, for the right job: it remains the cheapest MIT-licensed model in the GLM family with a 200,000-token window, and it is still served by most major providers. It is no longer Z.ai's flagship, since GLM-4.7, GLM-5, and GLM-5.3 have superseded it, so pick a newer model when you need the strongest scores. Pick GLM-4.6 when you need a capable, cheap, self-hostable coding model.