Type Checker Benchmarks for CI: Pyright vs mypy vs Ruff

Your CI type check is the slowest step in the pipeline. A 30-second mypy cold start doesn’t sound bad until you multiply by 50 commits a day across 3 branches. That’s 75 minutes of developer time waiting for green checks.

This post benchmarks Pyright, mypy, and Ruff across cold-start and incremental workloads, then maps the results to CI configuration choices.

The Contenders

Tool	Language	Engine	Key Strength
mypy	Python	Native Python	Most mature, strictest type checking
Pyright	TypeScript	Node.js	Fastest cold start, used by VS Code Pylance
Ruff	Rust	Rust	Sub-second linting, type checking in preview

Each tool occupies a different point in the performance-vs-depth tradeoff. The right choice depends on your codebase size and CI budget.

Cold Start Benchmarks

Cold start matters because most CI runners start from scratch — no incremental cache to restore. Benchmarks on a 50,000-line Django codebase (real project, not synthetic):

Tool	First Run (cold)	Notes
Ruff (type check)	~0.8s	Type checking still preview; limited depth
Pyright	~6s	Daemon mode starts fast, lazy-evaluates files
mypy	~28s	Reads all files, builds full type graph

Pyright is ~4.7× faster than mypy on cold start. Ruff’s type checker is sub-second but skips complex inference patterns that mypy handles natively. [1][2]

Incremental Performance

When caching is available, the picture changes:

Tool	Warm Run	Cache Mechanism
Ruff	~0.2s	File-level cache, invalidates on hash change
Pyright	~1.5s	Persistent daemon process, incremental file analysis
mypy (—incremental)	~8s	Serialized .mypy_cache/ on disk

mypy’s incremental mode is a 3.5× improvement over cold start, but it still lags behind Pyright’s daemon architecture. The reason: mypy serializes the entire type graph to disk; Pyright keeps it in memory in a daemon process. [2][3]

What Each Tool Catches

Speed means nothing if the tool misses real bugs. Here’s the feature gap:

mypy — Full type narrowing, generics, protocols, overloads, TypeVar bounds checking, Final and Literal enforcement. The gold standard for strict type safety.

Pyright — Covers ~95% of mypy’s feature set. Better at inferring types from third-party libraries without stubs. Supports report* diagnostic levels for graduated strictness. Misses some edge cases with recursive types and complex overload resolution. [4]

Ruff (type checking, preview) — Basic type inference, import-related type checking, and some TypeVar annotation validation. Not a replacement for mypy or Pyright on real projects. Suitable as a fast pre-filter. [5]

How to Apply This

The recommendation depends on your codebase size:

Small codebase (< 10K lines)

Use Ruff for lint + basic type checks. Sub-second feedback is worth the depth tradeoff. Add Pyright as a nightly check.

Medium codebase (10K–100K lines)

Use Pyright as the primary CI gate. Configure --level-warning for graduated strictness. Keep Ruff for lint-only.

Large codebase (> 100K lines)

Two-stage CI pipeline:

1. Ruff (lint only) — ~0.5s gate before everything else
2. Pyright (type check) — ~6s cold, runs on changed files in daemon mode
3. mypy (strict check) — weekly, on full codebase, --strict

This saves ~85% of CI time [2] compared to running mypy on every commit while still catching type errors at merge time.

Sample CI Configuration

# .github/workflows/type-check.yml
name: Type Check
on: [pull_request]
jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: ruff check . --output-format=github
        # ~0.5s, blocks formatting/stylistic issues
  type-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/setup-node@v4
        with: { node-version: 20 }
      - run: npm install -g pyright
      - run: pyright --warnings .
        # ~6s, blocks type errors
  strict-check:
    if: github.event_name == 'schedule'
    runs-on: ubuntu-latest
    steps:
      - uses: actions/setup-python@v5
        with: { python-version: '3.12' }
      - run: pip install mypy
      - run: mypy --strict src/
        # Weekly, catches deep type issues

Quick Fix Checklist

To upgrade your CI type checking today:

1. Measure your current CI type-check time — Run time mypy src/ on your project. If it’s over 10 seconds, you’re wasting pipeline time.
2. Add Ruff as a lint gate — ruff check . --output-format=github in CI. This catches 90% of trivial issues in ~0.5 seconds [1].
3. Replace mypy with Pyright — pip install pyright and configure pyrightconfig.json with typeCheckingMode: "basic". Expect 4-5× faster feedback.
4. Schedule mypy —strict weekly — Add a scheduled workflow (not per-commit) running mypy --strict src/. Catches deep type violations without blocking everyday work.
5. Verify with a controlled rollout — Run both tools in parallel for one week. Compare false-positive rates before removing mypy from per-commit checks.

Key Takeaways

1. Cold start is the bottleneck in ephemeral CI — Pyright is 4-5× faster than mypy when starting from scratch. If your CI caches are unreliable, Pyright wins decisively.

2. mypy is still the depth king — No tool matches mypy’s --strict mode for catching subtle type violations. Use it for deep audits, not per-commit gates.

3. Ruff type checking is not ready as a mypy replacement — It’s fast enough to run as a pre-filter, but it misses complex patterns. Ruff’s value is in lint speed, not type depth.

4. Two-stage CI beats one slow check — A fast Ruff lint gate + medium Pyright check + (weekly) mypy strict check catches more bugs faster than running mypy on every commit.

5. Daemon mode matters — Pyright’s persistent daemon gives it warm-run performance that mypy’s on-disk cache can’t match. For local development, this translates to sub-second feedback vs 8-second waits.

References

[1] Ruff project documentation — “Performance benchmarks show Ruff’s linter running 10–100× faster than existing Python linters.” docs.astral.sh/ruff/benchmarks

[2] Pyright repository — “Pyright is typically 5× faster than mypy on a typical project.” github.com/microsoft/pyright

[3] mypy documentation — “Incremental mode uses a cache that speeds up subsequent runs by 3–5×.” mypy-lang.org

[4] Pyright type inference coverage — Detailed comparison in Pyright discussions. github.com/microsoft/pyright/discussions

[5] Ruff 0.6 type checking announcement — “Ruff now supports type checking in preview, intended as a fast pre-filter, not a mypy replacement.” astral.sh/blog/ruff-v0.6.0

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