Little-coder more than doubles Qwen3.5-9B score
This post reports a benchmark comparison using the same Qwen3.5-9B Q4 weights under two different coding-agent scaffolds. On the 225-task Aider Polyglot benchmark, vanilla Aider scored 19.11% while little-coder reached 45.56% mean pass@2 across two full runs. The author argues that, at this scale, scaffold-model fit materially changes observed coding-agent performance, and that small local models may be underestimated by agent setups optimized for larger models.
Strong signal, but still an experiment-of-one. The hot take is that scaffold choice can matter as much as model choice for sub-10B coding agents, and this result is large enough to be attention-worthy even without paper-grade controls.
- –The claim is about scaffold adaptation, not a new model, which makes the comparison more interesting and more operationally useful.
- –The result is impressive, but the post itself notes missing replications, ablations, and broader model/benchmark coverage, so generalization is unproven.
- –The key engineering details are plausible: bounded reasoning budget, write guards, explicit workspace discovery, and smaller per-turn context injections all sound like good fits for constrained local models.
- –The biggest risk is overfitting the scaffold to Aider Polyglot or to a specific Qwen behavior profile; a second benchmark would help a lot.
DISCOVERED
4h ago
2026-04-19
PUBLISHED
6h ago
2026-04-19
RELEVANCE
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Creative-Regular6799