GLM-4.6 Guide: 200K Context and Fewer Tokens Per Task

The most useful number in the GLM-4.6 release was not a benchmark score. It was the claim that the model uses more than 30% fewer tokens than GLM-4.5 on average to do the same work. At an unchanged per-token price, that is a cost cut you get without negotiating anything, if it holds on your workload.

GLM-4.6 arrived at the end of September 2025, two months after GLM-4.5. This guide covers what changed, how to check the efficiency claim yourself, and where the model sits now that GLM-4.7 and the GLM-5 line exist.

What changed from GLM-4.5

Z.ai's model card lists five areas of improvement:

• Context: from 128K to 200K tokens. Z.ai's docs also give a 128K maximum output, up from 96K.
• Coding: higher scores on code benchmarks and better results in real agent tools, which the card names as Claude Code, Cline, Roo Code and Kilo Code, including more polished front-end output.
• Reasoning: clear gains, plus tool use during inference.
• Agents: stronger tool use and search-agent performance.
• Writing: style closer to human preference and more natural role-play.

The card lists 357B parameters and says inference works the same way as GLM-4.5, so existing vLLM or SGLang deployments carry over. Weights are MIT-licensed. Thinking is still switchable with thinking.type, and streaming responses return reasoning separately from the answer in reasoning_content.

The efficiency claim is the one to check

Z.ai's documentation says GLM-4.6 is more than 30% more efficient than GLM-4.5 in average token consumption. On its pricing page both models are listed at $0.60 per million input tokens and $2.20 per million output. OpenRouter listed GLM-4.6 at about $0.50 input and $2.00 output at the time of writing.

Why this matters more than a few benchmark points: in agent loops, output tokens dominate the bill, and reasoning tokens are output tokens. A model that reaches the same answer with a shorter chain of thought is cheaper per task even at an identical rate. Cost per agent run breaks down where the spend goes.

The claim is an average over Z.ai's tasks. Your ratio could be better or worse, especially if your prompts already run with thinking disabled, in which case there is less reasoning to trim.

CC-Bench: a vendor benchmark you can inspect

Z.ai evaluated GLM-4.6 on 74 real-world coding tasks run inside Claude Code. Against Claude Sonnet 4 it reports a 48.6% win rate, 9.5% ties and 41.9% losses, which Z.ai itself calls near parity rather than a win, and clearer leads over GLM-4.5, Kimi-K2-0905 and DeepSeek-V3.1-Terminus. On eight public benchmarks covering agents, reasoning and coding, it describes the model as competitive with DeepSeek-V3.1-Terminus and Claude Sonnet 4.

The same dataset puts a number on the efficiency claim for this task set: GLM-4.6 averaged about 651K tokens per task against about 763K for GLM-4.5, a 14.6% reduction. That is well short of the 30% headline, which is a useful reminder that the saving depends on the work.

What makes CC-Bench more useful than most vendor evaluations is that Z.ai published the task prompts and the agent trajectories on Hugging Face. You can read what the model actually did, turn by turn, rather than trusting a win count. That is worth an hour of anyone's time before adopting the model: look at the failures and ask whether they resemble the tasks you care about.

For a sense of the gap to its successor, Z.ai reports GLM-4.7 at 73.8% on SWE-bench Verified and describes that as 5.8 points above GLM-4.6.

200K in practice

Going from 128K to 200K is not a doubling, but it moves a specific class of work from awkward to comfortable: medium-sized repositories loaded whole, agent sessions that run past a few dozen tool calls before compaction, and long specifications plus code in one prompt.

The usual caveat applies. Advertised context is not the same as context the model attends to well, and quality near the middle of a long window tends to drop. Context length vs effective context covers how to measure the usable part rather than trusting the label.

GLM-4.6 versus GLM-4.7 at the same price

Here is the awkward part for GLM-4.6. Z.ai's pricing page lists GLM-4.7 at the same $0.60 and $2.20 per million tokens, with the same 200K context and 128K output. Z.ai reports gains for GLM-4.7 across SWE-bench Verified, Terminal Bench 2.0 and other agent benchmarks, along with finer thinking controls.

So the case for GLM-4.6 in late 2026 looks a lot like the case for GLM-4.5 a year ago:

• you have a validated prompt suite and regression tests built against it;
• you have fine-tuned adapters on its weights;
• a third-party host serves it at a better rate than its successor, which happens as older models get commoditised.

If none of those apply, start a new project on a newer model. If you are moving up from GLM-4.5, the GLM-4.5 guide covers what to freeze before the switch.

How to measure the upgrade yourself

1. Pick twenty tasks from your history that ran with thinking enabled.
2. Run them on GLM-4.5 and GLM-4.6 with identical prompts, tools and sampling. Z.ai suggests temperature 1.0 for general use.
3. Log three numbers per task: completed or not, total output tokens including reasoning, and turns taken.
4. Divide total spend by completed tasks for each model.

If the token saving is real for you, it shows up in step four. Then repeat the exercise against GLM-4.7 before settling.