Fixed prompts
Every text task uses a fixed brief or source. Changing the prompt makes the result non-comparable.
Benchmark Lab
This is a small, reproducible benchmark suite for the jobs K3.1 Studio actually serves: project scaffolding, code review, document extraction, agent planning, and screenshot analysis. Run a task, score it against the rubric, then publish the artifact.
Round 0 status: protocol and sharing are live. Comparative scores will be summarized only after enough public runs exist. We do not publish invented benchmark numbers.
Every text task uses a fixed brief or source. Changing the prompt makes the result non-comparable.
A run is evidence only when its full visible output can be opened and exported by other people.
Each task is scored 0–4 for the dimensions that matter for real project work, not vibes.
Failed and mediocre runs can also be shared. The log is ordered by time, not by score.
coding
Measures: Completeness · Technical accuracy · Actionability · Code quality
Benchmark task: create a production plan from this fixed brief. Brief: Build a multi-tenant analytics dashboard for a small B2B SaaS. It must ingest CSV exports, store events in Postgres, show retention and revenue charts, support role-based access, and export a monthly report. Return: 1. Architecture decisions and trade-offs 2. Data model with tables and relationships 3. File map for a Next.js + Postgres implementation 4. Implementation milestones 5. Top five risks and mitigations 6. Starter SQL for the most important table
coding
Measures: Recall · Severity calibration · Evidence · Fix quality
Benchmark task: review this fixed code and identify correctness, security, and maintainability risks.
```ts
export async function loginUser(email: string, password: string) {
const user = await db.user.findFirst({ where: { email } });
if (!user) throw new Error("Invalid user");
const ok = await bcrypt.compare(password, user.passwordHash);
if (!ok) throw new Error("Invalid password");
const token = crypto.randomBytes(12).toString("hex");
await db.session.create({ data: { token, userId: user.id } });
return { token, email: user.email, credits: user.credits };
}
```
Return a table with Severity, Issue, Evidence, Why it matters, Fix, and Effort. Then list the three fixes you would make first.analysis
Measures: Fact recall · Constraint extraction · Ambiguity detection · Clarity
Benchmark task: extract structured facts from this fixed source text. Source: "The customer will receive 100 free credits during early access. Credits do not expire while the service remains available. The provider may throttle accounts that exceed 20 model requests per minute. Uploaded files are used only to complete the requested task and are not used for training. Either party may terminate the pilot with seven days' written notice. Paid credit packs, if introduced later, will be sold as one-time purchases rather than subscriptions." Return: 1. Facts table 2. Obligations 3. Restrictions 4. Ambiguities 5. Questions a buyer should ask before paying
planning
Measures: Task decomposition · Tool fit · Success criteria · Failure handling
Benchmark task: turn this fixed goal into an executable agent plan. Goal: Research five competitors for an independent Kimi K3.1 Pro workbench, compare their pricing and workflow strengths, then recommend one differentiation strategy and one paid package. For each step return: objective, required inputs, recommended tool, expected output, success criteria, and failure mode. Keep the plan executable and testable by an AI agent.
analysis
Measures: Visual recall · Component structure · Accessibility · Implementation clarity · requires one image attachment
Benchmark task: attach one UI screenshot and convert it into a frontend specification. Return: 1. Component hierarchy 2. State and data requirements 3. Responsive behavior 4. Accessibility risks 5. Tailwind-style markup sketch 6. Questions raised by the screenshot