TL;DR: TSK-1 has tested one of these families and not the other — Kimi K3 has a result from Jul 31, 2026 (fewest failed steps of the models tested that day, 7.2%), and Qwen has not been tested yet. On published evidence: Qwen's Apache 2.0 ladder spans 0.8B to 397B with a multimodal 35B-A3B, while Kimi ships 2.8T open weights and a 1,048,576-token context. Route both inside Taskade Genesis rather than standardizing on one.
What TSK-1 Found
TSK-1 hasn't run these two head-to-head yet; here's what public benchmarks show. Kimi K3 has a result on record from Jul 31, 2026: the fewest failed steps of the models tested that day at 7.2%, with the fewest steps overall and the most accurate account of its own work. Qwen is listed as available in the dataset: open-weight, Apache 2.0, not tested yet, with vendor-published figures of SWE-bench Verified 73.4% and MMLU-Pro 85.2% for Qwen3.6-35B-A3B. Until a head-to-head test runs, this pairing rests on published benchmarks, license terms, and published details, not on a real build we ran and opened ourselves.
- Qwen: Not yet tested by TSK-1; listed as available only. Public figures: SWE-bench Verified 73.4%, MMLU-Pro 85.2% (Qwen3.6-35B-A3B, vendor-published).
- Kimi: Jul 31, 2026 — fewest failed steps of the models tested (7.2%), fewest steps overall, most accurate account of its own work.
See the full evidence at /tsk/qwen, /tsk/kimi, and the TSK-1 hub.
Qwen 3.6 vs Kimi K3
This comparison rests on published details, public benchmarks, and one family's TSK-1 record — not on a controlled head-to-head, and the honest thing is to say so up front. Qwen's open generation is Qwen3.6: a 27B dense model and a 35B-A3B mixture-of-experts model that is multimodal via a vision encoder, both under Apache 2.0, with an API-only line running a generation ahead. Kimi K3 is Moonshot's flagship: 2.8 trillion total parameters with 104 billion used per token, a 1,048,576-token context, image-text-to-text input, and downloadable weights under the Kimi K3 License.
The evidence splits by type. Kimi K3 has been tested: on Jul 31, 2026 its steps failed least often of the models tested that day at 7.2%, it took the fewest steps, and it gave the most accurate account of its own work. Qwen has not been tested yet — its published strengths are distribution and fit, including SWE-bench Verified 73.4% and MMLU-Pro 85.2% for Qwen3.6-35B-A3B, vendor-published on a 35B-total model using 3B per token.
Choose Qwen If…
A comparison that never concedes anything is not worth reading. Against a single very large flagship, Qwen is the better pick in several common cases.
- A 2.8-trillion-parameter model is out of reach. Kimi K3's weights are downloadable but the deployment is a serious multi-GPU exercise. Qwen3.6-35B-A3B uses 3 billion parameters per token and runs on one consumer GPU, so "open weights" translates into something you can actually host.
- You want a license your legal team has already read. Apache 2.0 with no size exceptions covers the whole open ladder; the Kimi K3 License is bespoke and needs reading before you redistribute anything built on it.
- The pipeline mixes model sizes. Qwen gives you an exit at every rung from 0.8B to 397B, so bulk classification and heavy reasoning can run on the same family at different costs rather than on one flagship for both.
- You are fine-tuning and shipping the result. Apache 2.0 puts essentially no conditions on a redistributed fine-tune, which is the difference between an experiment and a product.
Choose Kimi If…
- The agent runs long chains of actions. The fewest failed steps of the models tested on Jul 31, 2026 (7.2%) is measured evidence from real builds — not a vendor claim.
- You want the largest open-weight model available. 2.8 trillion total parameters with 104 billion used per token is the top of the open-weight range.
- Your inputs include images. Kimi K3 is multimodal, taking image and text input.
- You want TSK-1 evidence now, not later. Kimi has a result 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 mean trading a tested agent model for a deployable size ladder, when a real workflow usually wants both: something reliable through a long chain of actions, and something small enough to run the bulk work cheaply.
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 bulk extraction step on a small Qwen model and an action-heavy agent step on Kimi K3 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, reliable model executes. Bulk classification on the smallest capable Qwen rung; long agent runs on Kimi K3's measured reliability.
- Vision on the open model, long context on the flagship. Qwen's multimodal rung for image and video input; Kimi K3 for million-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: Measured Reliability vs Deployment Range
Kimi K3 is the one with controlled evidence behind it: the fewest failed steps of the models tested on Jul 31, 2026, the fewest steps to a finished build, 2.8 trillion open-weight parameters, and a 1,048,576-token context. Qwen is the one with range: an Apache 2.0 ladder from 0.8B to 397B, a multimodal rung that fits a single GPU, and a test still to come.
Measured reliability and deployment range are not the same purchase, and most setups need both. Route per task, and check back after Qwen's TSK-1 test lands — this page upgrades from public benchmarks to controlled evidence in one dataset edit.
▲ Memory feeds Intelligence. ■ Intelligence triggers Execution. ● Execution creates Memory. A tested flagship and a full ladder. One workspace. Model choice stays a setting, not a rebuild.
This is the origin of living software. 🌱
Build with Qwen and Kimi in one workspace →
Related reading
- Moonshot Kimi history — How the Kimi line evolved.
- 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.
- Qwen vs DeepSeek — Apache 2.0 versus MIT, head to head.
- Qwen vs GLM — The other Qwen pairing.
- Kimi vs DeepSeek — Bespoke license 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 Qwen profile — Full profile and public-benchmark aggregation for Qwen.
- TSK-1 Kimi profile — Full benchmark evidence for the Kimi family.
- TSK-1 hub — The complete model benchmark dataset.