TL;DR: Same test, different wins. DeepSeek V4 Flash built the best-looking app on Aug 1, 2026 — light-and-dark theme, zero errors, clean on a phone, at a fraction of the field's cost. GLM-5.2 started saving your data properly in the same test and was the only model to refuse a login screen nobody asked for (Jul 30, 2026). Route by task inside Taskade Genesis rather than standardizing on one.
What TSK-1 Found
We ran both families in the same tests, and the findings split by measure. DeepSeek V4 Flash built the best-looking app on Aug 1, 2026 at a fraction of the field's cost, and on Aug 3, 2026 it posted the cheapest, fastest, and cleanest run we have measured. GLM-5.2 produced the cleanest behavioral finding we have recorded: refusing the login screen nobody asked for (Jul 30, 2026). Its data saving arrived on Aug 1, 2026, in the same test DeepSeek won on looks.
- DeepSeek: Aug 1, 2026 — best-looking app; Aug 3, 2026 — cheapest, fastest, and cleanest run measured; Aug 6, 2026 — most of your data wired up of any model measured (a record 60 fields, 8 automations, all four wordings word for word).
- GLM: Jul 30, 2026 — refused the login screen nobody asked for, the only model to push back on quietly added work; Aug 1, 2026 — it saved your data properly, with a cosmetic theme issue as the only thing left open.
See the full evidence at /tsk/glm, /tsk/deepseek, and the TSK-1 hub.
GLM-5.2 vs DeepSeek V4 Flash
These two met in the same nine-model test, and the distance between them is narrower than the price gap suggests. DeepSeek V4 Flash won Aug 1, 2026 outright on looks: the only build with a coherent light-and-dark theme, zero errors, and a clean 390px layout on a phone — and it did it at a fraction of the field's cost. GLM-5.2 was in the same test, and its story there is a near-miss: it saved your data properly for the first time — it had not managed that on Jul 30, 2026 — with a cosmetic theme issue as the only thing left open. A cosmetic theme defect is exactly the class of issue this testing is there to catch before a build ships.
The telling detail is that both families now save your data properly. GLM went from doing none of it on Jul 30, 2026 to a working setup on Aug 1, 2026. DeepSeek took the looks win again on Aug 2, 2026 with its data saving checked end to end — eleven files, and both themes checked out. Both ship MIT weights, so the economics question is the rate card rather than the license: DeepSeek V4 Flash is the cheaper of the two on published rates, and its efficiency record stands — cheapest, fastest, and cleanest run on a real customer's request (Aug 3, 2026).
GLM-5.2 vs DeepSeek V4 Pro
One rung up, the split becomes judgment versus breadth. GLM-5.2's defining moment happened before it could save your data at all: on Jul 30, 2026 it thought about adding a login screen nobody had asked for and declined, offering an "Add Login" suggestion instead — the only model in that test to push back on quietly added work. That is a judgment finding no parameter count captures.
DeepSeek V4 Pro's defining moments are breadth and finishing what it starts. On Aug 6, 2026 it wired 8 automations and a record 60 fields, reproducing all four of the customer's wordings word for word — the richest workspace memory of that test. On Aug 5, 2026 it won the tracker build as the cheapest app that actually opened and ran, even though the raw numbers pointed elsewhere, and it holds the biggest single-test jump we have measured in building what was asked for: 0 of 4 to 4 of 4 word for word on a bare-bones setting (Aug 2, 2026).
Choose GLM If…
A comparison that never concedes anything is not worth reading. GLM is the better pick in several common cases.
- The request is loose and scope-creep is a real risk. GLM is the model that asked before it added a login screen nobody wanted (Jul 30, 2026).
- Long-horizon engineering is the job. GLM-5.2 is positioned for exactly that, with a 1M-token context and 128K max output.
- You want MIT weights and a supported endpoint. GLM-5.2's published weights are MIT, and Zhipu also publishes per-token pricing and cached-input rates on z.ai — so you can self-host, call the API, or move between them without a license renegotiation.
- You want correct behavior graded by an outside test. The Jul 30, 2026 finding is the standing evidence.
Choose DeepSeek If…
- Visual polish on a budget is the job. Aug 1, 2026 is the standing evidence: the cheapest model in the test produced the best-looking app.
- A single step has to emit a lot at once. DeepSeek V4's 384K maximum output is triple GLM-5.2's 128K ceiling, which is the difference between generating a whole file set in one call and stitching several together.
- You are wiring up a lot of your data. The record 60 fields and 8 automations of Aug 6, 2026 are the most of your data any model has wired up for us.
- Your volume is high and your budget is tight. The published rate card is the cheapest on this page, and off-peak billing is exactly half of peak. Peak is 01:00-04:00 and 06:00-10:00 UTC on weekdays (as of August 2026), so scheduled batch work can sit entirely outside it.
The Taskade Angle: Route, Don't Standardize
Most comparison pages end with "pick one". The evidence for these two open-weight families points the other way: one owns looks and value, the other owns doing the right thing with a loose request. Serious teams run both and route between them.
Taskade routes across 15+ frontier models from OpenAI, Anthropic, Google, 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 design pass on V4 Flash, a data-wiring pass on V4 Pro, and an ambiguity-sensitive pass on GLM-5.2 can each get the model that leads there. 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:
- Cheap model iterates, judgment model sanity-checks. Design loops on V4 Flash; scope decisions and unclear requests on GLM-5.2.
- Breadth model wires, behavior model guards. A data-rich build on V4 Pro, with GLM on the steps where quietly added scope is the risk.
- Every step lands in the same project graph. Whichever model runs a step, the result becomes shared workspace memory, 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: Judgment vs Value
GLM-5.2 is the judgment pick, the model that refused work nobody asked for, now saving your data properly with a cosmetic theme issue as its only open item. DeepSeek V4 is the value pick — the best-looking app at a fraction of the field's cost, the cheapest and fastest run we have measured, and the most of your data wired up, on the cheapest published rates here. Both families ship MIT weights, so the choice between them is about price, output ceiling, and which measure you need — not about licensing.
Neither is the winner. The winner is the setup that puts judgment where ambiguity lives and value where volume lives.
▲ Memory feeds Intelligence. ■ Intelligence triggers Execution. ● Execution creates Memory. Two open-weight families. One workspace. No single point of vendor failure.
This is the origin of living software. 🌱
Build with GLM and DeepSeek in one workspace →
Related reading
- 10 Best Open-Source AI LLMs in 2026 — Where both families sit in the open-weight field.
- History of AI benchmarks — Why every model claims to be best, and how TSK-1 differs.
- Kimi vs DeepSeek — Bespoke license versus MIT, head to head.
- Kimi vs GLM — The open-weight hygiene-versus-judgment pair.
- Qwen vs DeepSeek — Apache 2.0 versus MIT.
- 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 GLM profile — Full evidence for the GLM family.
- TSK-1 DeepSeek profile — Full evidence for the DeepSeek family.
- TSK-1 hub — The complete model test dataset.