TL;DR: TSK-1 hasn't run Qwen and GLM head to head. GLM has results on record from Jul 30 and Aug 1, 2026. On published evidence: Qwen's Apache 2.0 ladder spans 0.8B to 397B with a multimodal 35B-A3B, while GLM-5.2 ships a native 1M-token context and vendor-published coding scores. Route both inside Taskade Genesis.
What TSK-1 Found
Both now have TSK-1 results on record; here's what public benchmarks add. GLM-5.2 has results on record: on Jul 30, 2026 it was the only model that refused to add a login screen nobody had asked for, and by Aug 1, 2026 it was genuinely saving your data. Qwen was tested on Aug 25, 2026 and scored 50 out of 100; its dated findings are on the Qwen page. The rest of this page rests on published vendor benchmarks, license terms, and published details.
- Qwen: Tested by TSK-1 on Aug 25, 2026, scoring 50 out of 100. Public figures: SWE-bench Verified 73.4%, MMLU-Pro 85.2% (Qwen3.6-35B-A3B, vendor-published).
- GLM: Jul 30, 2026 — refused to add the login screen nobody asked for. Aug 1, 2026 — genuinely saving your data, with a cosmetic styling issue still open. Vendor-published: Terminal-Bench 2.1 81.0, SWE-bench Pro 62.1.
See the full evidence at /tsk/qwen, /tsk/glm, and the TSK-1 hub.
Qwen 3.6 vs GLM-5.2
The real split here is shape, not score: one family ships a ladder of sizes, the other ships one flagship behind a managed API. Qwen's open generation spans a 27B dense model and a 35B-A3B mixture-of-experts model with a vision encoder, both Apache 2.0, with an API-only line running a generation ahead that you cannot download at all. GLM-5.2 is a single flagship with MIT-licensed weights, text-to-text, tuned for long-horizon engineering with a native 1M-token context and 128K maximum output, and documented primarily against the z.ai API. That difference decides how you deploy long before any benchmark number does — and TSK-1 has not run these two head to head yet, so the honest framing is published details plus one family's test record, not a controlled duel.
The public benchmark numbers come from different setups and should not be read as a direct duel. Qwen3.6-35B-A3B posts SWE-bench Verified 73.4% and MMLU-Pro 85.2% — a 35B-total model using 3B per token. Zhipu publishes GLM-5.2 at Terminal-Bench 2.1 81.0 and SWE-bench Pro 62.1. Different tests, different dates. What both figures agree on is that open-weight models now sit close enough to the frontier that choosing between them is a distribution and fit decision, not a capability cliff.
Choose Qwen If…
A comparison that never concedes anything is not worth reading. Against a single managed flagship, Qwen is the better pick in several common cases.
- You need to place the model on hardware you control. Qwen publishes a size ladder from 0.8B to 397B, so there is a rung that fits the GPU you already have. GLM-5.2's weights are equally permissive, but its parameter count is not published in comparable detail and Zhipu documents the managed API on z.ai as the path — the constraint is sizing information, not licensing.
- You need vision without leaving the open weights. Qwen3.6-35B-A3B carries a vision encoder in the open ladder; GLM keeps vision in the separate GLM-V line, so a multimodal step means a second model either way.
- The permissive terms cover every rung, not one model. Apache 2.0 with no size exceptions means one legal review covers the 0.8B rung and the 397B rung alike — useful when a pipeline mixes sizes, where GLM's equally permissive MIT applies to a single flagship.
- You want the deepest derivative ecosystem. 700M+ Hugging Face family downloads and 113,000+ derivative models mean quantizations, fine-tunes, and deployment recipes already exist for most rungs, per Hugging Face figures.
Choose GLM If…
- Long-horizon engineering is the job. GLM-5.2 is positioned for exactly that, with a native 1M-token context and 128K max output.
- You want MIT weights with a supported endpoint behind them. GLM-5.2's published weights are MIT, and Zhipu publishes per-token pricing and cached-input rates on z.ai.
- Measured behavior matters to you. GLM is the only model on record that refused to add a login screen nobody had asked for (Jul 30, 2026), and it has been genuinely saving your data since Aug 1, 2026.
- You want TSK-1 evidence now, not later. GLM has results on record; Qwen has not been tested yet.
The Taskade Angle: Route, Don't Standardize
Most comparison pages end with "pick one". On this pairing that would collapse two separate decisions into one: which size of model your hardware can hold, and which model you want reasoning across a million tokens. Those are different questions, and a workspace that routes per step lets you answer them separately.
Taskade routes across frontier models from top AI labs inside one workspace, with the AI allowance included in the subscription rather than a separate API account per lab. Paid plans start at Pro $10/mo billed annually, with Business $25, Max $100 and Enterprise $250 per month billed annually. You set the model per agent or per automation step, so a bulk extraction step on a small Qwen model and a long-horizon reasoning step on GLM-5.2 can each get the model that fits. Leave a step on TSK-1 Auto and it adapts the depth instead — fast when the step is quick, deeper reasoning when it is not.
Four patterns that hold up:
- Small model triages, large model resolves. Bulk work on the smallest capable Qwen rung; escalations route up to GLM-5.2 or the closed frontier.
- Vision on the open model, text on the long-context model. Qwen's multimodal rung for image and video input; GLM-5.2 for 1M-token reasoning.
- Every step lands in the same project graph. Whichever model runs a step, the result becomes Workspace DNA, so the next agent inherits context instead of re-deriving it.
- Scheduled automations read from the same place. Model choice becomes a per-step setting, not a platform decision.
See 10 Best Open-Source AI LLMs in 2026 for how both families sit in the wider open-weight field.
Final Word: Fit the Hardware or Fit the Horizon
Qwen answers the hardware question: an Apache 2.0 ladder from 0.8B to 397B, a multimodal rung that fits one consumer GPU, and a derivative ecosystem deep enough that most deployment problems are already solved. GLM answers the horizon question: a native 1M-token context with 128K output, MIT weights behind a published managed rate card, and the only model we have ever seen refuse to add a login screen nobody asked for.
Most real workflows ask both questions in the same week. Route per task, and check back after the TSK-1 head-to-head lands — what we can say here upgrades from public benchmarks to controlled evidence in one dataset edit.
▲ Memory feeds Intelligence. ■ Intelligence triggers Execution. ● Execution creates Memory. A ladder and a flagship. One workspace. Model choice stays a setting, not a rebuild.
This is the origin of living software. 🌱
Build with Qwen and GLM in one workspace →
Related reading
- 10 Best Open-Source AI LLMs in 2026 — Where both families sit in the open-weight field.
- Qwen vs DeepSeek — Apache 2.0 versus MIT, head to head.
- Qwen vs Kimi — The other Qwen pairing.
- Kimi vs GLM — The open-weight hygiene-versus-judgment pair.
- Multi-Model AI Access — How Taskade routes across providers.
- Multi-Agent Teams — Specialists with different model picks.
- Taskade MCP Server — Connect any MCP-compatible IDE to your workspace.
- TSK-1 Qwen profile — Full profile and public-benchmark aggregation for Qwen.
- TSK-1 GLM profile — Full benchmark evidence for the GLM family.
- TSK-1 hub — The complete model benchmark dataset.