TL;DR: Agent builds are decided by whether the AI's own steps succeed, and that is what TSK-1 checks first. On 31 July 2026 (1-3 builds per model), Kimi K3 led with 7.2% of its steps failing and the fewest steps overall; MiniMax M3's steps failed 47.2% of the time and it did not place that day. Screen your own work on the same four points, route per step inside Taskade Genesis, and check everything that ships.
What TSK-1 Found
TSK-1 treats step reliability as pass-or-fail, not as a bonus, because a model that cannot finish its own chain of actions cannot finish a build. The test ran on 31 July 2026 at 1-3 builds per model — directional evidence, not an absolute ranking — and it separated these two families on exactly that point. Kimi K3's steps failed least often of the models tested that day, at 7.2%, with the fewest steps and the most accurate account of its own work. MiniMax M3's steps failed 47.2% of the time on those builds, which did not clear the bar, so the family did not place rather than being scored. We re-test when a new version ships.
- Kimi: Jul 31, 2026 — fewest failed steps of the models tested (7.2%), fewest steps overall, most accurate account of its own work.
- MiniMax: Jul 31, 2026 — steps failed 47.2% of the time, below the reliability bar; did not place, pending a fresh test.
See the full evidence at /tsk/minimax, /tsk/kimi, and the TSK-1 hub.
What Reliable Steps Actually Buy You
When you build a real app with an agent, the model is not answering a question — it is running a loop. It reads files, writes files, runs checks, and reads the result of each action before deciding the next one. A single chat turn tolerates a mistake because a human is reading the output. A forty-step build does not: every failed action burns a step, leaves the job half-finished in the model's working memory, and pushes it further from what you asked for. That compounding is why a model can be excellent in conversation and still stall an agent loop.
This is what TSK-1 checks first. It does not score a build on how it reads — the app has to open and run, actually save your data to a database, and survive a scripted visitor who fills in the app's form while every saved field is checked one by one. How often a model's steps fail predicts all of that, so it is measured first and treated as pass-or-fail rather than as a tiebreaker.
On 31 July 2026, that check did its job in both directions. Kimi K3 cleared it comfortably and led on the surrounding measures too: the fewest steps to a finished build, and a closing summary that stated accurately what it had and had not built. MiniMax M3's steps failed 47.2% of the time on those builds and it did not clear the bar. Both readings come from 1-3 builds per model on 31 July 2026 — enough to steer a routing decision, not enough to settle a family's ceiling, which is why we re-run families as new versions ship.
Choose MiniMax If…
A comparison that never concedes anything is not worth reading. MiniMax is the better pick in several common cases.
- The job spans modalities. MiniMax's lineup covers text, speech, music, image, and the Hailuo video line under one platform — breadth that is hard to assemble from a single text-only lab.
- You want published API pricing with a discount tier. M3's standard rate carries a 50% reduction from list that MiniMax publishes as permanent, and Token Plans start at $20 per month.
- The step does not depend on a long chain of actions. For chat, drafting, and single-shot generation, the Jul 31, 2026 finding is not the constraint that matters.
- You are screening, not committing. MiniMax is available in Taskade, so you can run the four-point screen on your own prompts and re-run it when a new version ships.
Choose Kimi If…
- The agent runs long chains of actions. Fewest failed steps of the models tested on Jul 31, 2026 (7.2%) is the single best predictor of a smooth agent build.
- You want the largest open-weight model available. 2.8 trillion parameters in total, with 104 billion used per token, under the Kimi K3 License.
- Your inputs include images. Kimi K3 is multimodal, taking image and text input.
- You are self-hosting. Downloadable weights — read the license before redistributing.
The Taskade Angle: Route, Don't Standardize
Most comparison pages end with "pick one". The evidence for this pairing points to a sharper rule: pick per step, and check everything that ships.
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 action-heavy agent steps get a model that clears the reliability screen, multimodal steps get MiniMax's lineup, and a final check runs over whatever either one produces before it ships. 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:
- Action-heavy loops on the screened model, multimodal on the specialist. Agent work on Kimi K3; image, video, and audio steps on MiniMax's lineup.
- Screen before you standardize. Run the four-point checklist — how often steps fail, step count, honest summary, and a check on the result — on your real prompts before any model becomes your default.
- 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 Kimi sits in the wider open-weight field.
Final Word: Reliability Decides It
Kimi K3 is the step-efficient open-weight flagship — the fewest failed steps of the models tested on 31 July 2026, with downloadable weights and a million-token context. MiniMax is the multimodal breadth play — a full text, speech, image, and video lineup behind published pricing, best paired with a reliability screen on the agent steps specifically.
Neither is the winner. The winner is the setup that screens every model on the same four points, routes each step to the model that leads on that measure, and checks everything that ships — so a routing decision stays a setting you can change, not a migration.
▲ Memory feeds Intelligence. ■ Intelligence triggers Execution. ● Execution creates Memory. Two model families. One workspace. One standard.
This is the origin of living software. 🌱
Build with MiniMax and Kimi in one workspace →
Related reading
- Moonshot Kimi history — How the Kimi line evolved.
- 10 Best Open-Source AI LLMs in 2026 — Where Kimi sits 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.
- 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 MiniMax profile — Full benchmark evidence for the MiniMax family.
- TSK-1 Kimi profile — Full benchmark evidence for the Kimi family.
- TSK-1 hub — The complete model benchmark dataset.