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Benchmark Lab

First-party Kimi K3.1 Pro workbench tests

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 We do not publish invented benchmark numbers.

Evidence rules

Method

Fixed prompts

Every text task uses a fixed brief or source. Changing the prompt makes the result non-comparable.

Method

Public artifacts

A run is evidence only when its full visible output can be opened and exported by other people.

Method

Transparent rubric

Each task is scored 0–4 for the dimensions that matter for real project work, not vibes.

Method

No hidden cherry-picking

Failed and mediocre runs can also be shared. The log is ordered by time, not by score.

Five fixed tasks

coding

Project scaffold from a fixed brief

Run in Studio

Measures: Completeness · Technical accuracy · Actionability · Code quality

Fixed prompt
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
Scoring rubric
  • 0 = misses the brief or invents requirements
  • 1 = relevant but mostly generic
  • 2 = addresses the brief with minor gaps
  • 3 = complete, specific, and implementable
  • 4 = implementable, production-aware, and clearly scoped

coding

Code risk review from a fixed snippet

Run in Studio

Measures: Recall · Severity calibration · Evidence · Fix quality

Fixed prompt
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.
Scoring rubric
  • 0 = no real issues found
  • 1 = finds obvious issues only
  • 2 = finds security and correctness issues
  • 3 = prioritizes accurate fixes with evidence
  • 4 = also covers maintainability and production edge cases

analysis

Document extraction from a fixed source

Run in Studio

Measures: Fact recall · Constraint extraction · Ambiguity detection · Clarity

Fixed prompt
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
Scoring rubric
  • 0 = paraphrases inaccurately
  • 1 = misses key constraints
  • 2 = extracts most facts
  • 3 = extracts all material facts and ambiguities
  • 4 = also turns the facts into practical buyer questions

planning

Agent task plan from a fixed goal

Run in Studio

Measures: Task decomposition · Tool fit · Success criteria · Failure handling

Fixed prompt
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.
Scoring rubric
  • 0 = vague brainstorm
  • 1 = steps lack inputs or outputs
  • 2 = executable basic plan
  • 3 = clear success criteria and tool choices
  • 4 = robust plan with failure modes and verification

analysis

Screenshot to frontend spec

Run in Studio

Measures: Visual recall · Component structure · Accessibility · Implementation clarity · requires one image attachment

Fixed prompt
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
Scoring rubric
  • 0 = invents invisible UI
  • 1 = generic description
  • 2 = accurate structure
  • 3 = implementable spec with states and responsiveness
  • 4 = production-ready accessibility and edge-case notes

Public run log

No benchmark artifact has been shared yet. Complete a task above, click Create public link in Studio, and it will appear here.

Known limitations