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 15+ frontier models from OpenAI, Anthropic, and open-weight providers 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.
