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ProgramBench

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2

RESEARCH · 1 SOURCE · Cohere

Study: delegating exploration and planning lets agent teams reach near-frontier coding performance at far lower cost

A controlled study of coding-agent teams finds that well-organized multi-agent systems can outperform single agents on ProgramBench by up to ~10 percentage points but typically cost 3–7× more. Crucially, delegating only exploration and planning can match the performance of delegating planning, implementation, and verification together at roughly half the cost — e.g., a Sol lead with 16 inexpensive Luna planning workers reached 61.8% reward vs 63.2% for an all‑Sol team while cutting API cost from $510 to $155; the paper also situates these results amid recent large multi-agent coding projects (Claude, Cursor, Bun).

7.0

RESEARCH · 1 SOURCE · The Decoder

Vals AI: agent teams cost far more for minimal quality gains

Evals firm Vals AI benchmarked GPT-6 Sol and Claude Opus 5.5 on the Vibe Code Bench as solo agents and in multi-agent teams at medium and maximum reasoning effort. Teams consumed 1.8×–5.1× more tokens but showed almost no consistent quality improvement—only GPT-6 Sol at medium reasoning produced a statistically significant +7.3 score—while at maximum reasoning teams offered no real advantage; Anthropic, ProgramBench tests, and OpenAI researchers similarly report that agent swarms mainly speed up work at higher cost and face coordination limits.

6.0