Everyone's piling on about the new open-source model support, especially the DeepSeek-Coder integration. "It's free!" "It's competitive with GPT-4!" I'm seeing a lot of hype and very few numbers.
Has anyone actually *benchmarked* it against the usual suspects (Claude, GPT-4, even CodeLlama) for real-world Continue tasks? I'm talking about:
* **Completion accuracy** in a large, messy monorepo, not a clean single-file example.
* **Refactoring reliability** for non-trivial changes (e.g., "extract this logic into a new microservice interface").
* **Understanding project-specific patterns** and linter rules.
I tried it on a decent-sized Go backend. The initial completions were fast, but the moment I asked for something involving multiple packages, it started hallucinating imports and inventing types. My standard test is to ask it to write a database repository layer with context-timeout handling and transaction rollback logic. The results were... mixed.
```go
// It gave me something like this. Looks plausible at a glance, but...
func (r *Repo) UpdateItem(ctx context.Context, item *Item) error {
ctx, cancel := context.WithTimeout(ctx, r.timeout)
defer cancel()
tx, err := r.db.BeginTx(ctx, nil)
if err != nil {
return err
}
// Missing rollback on later error. It got the structure but missed a critical detail.
_, err = tx.ExecContext(ctx, "UPDATE items SET ...", item)
if err != nil {
return err // Oops. Leaking transaction here.
}
return tx.Commit()
}
```
The promise of a truly free, high-quality model is enticing, but I'm skeptical until I see:
* Comparative latency measurements under load.
* Side-by-side code review quality assessments.
* Its failure modes when the codebase is poorly documented.
Is this just a case of "good enough for free," or does it actually hold up under scrutiny? I want to see your concrete examples and, preferably, your benchmarks.
profile before you optimize