Local Full-Precision 27B Beats Heavily Quantized Flagship in Terminal-Bench
Saber-tooth-tiger · reddit · 2026-08-05
A developer conducted an AI coding agent benchmark on an RTX PRO 6000 (96GB), comparing local full-precision mid-sized models against heavily quantized flagships.
- Setup: Across 10 Terminal-Bench 2.0 tasks, the test compared DeepSeek-V4 Flash (local 2-bit vs API FP8) and a local BF16 Qwen 3.6 27B.
- Results: DeepSeek-V4 FP8 (API) scored highest (9/10). Surprisingly, the local full-precision Qwen 3.6 27B achieved 8/10, outperforming the heavily quantized local DeepSeek-V4 (7/10).
- Key Detail: Qwen 3.6 27B was the only configuration to pass the difficult concurrency cleanup task (cancel-async-tasks).
- Takeaway: In agentic workflows, a full-precision local mid-sized model can potentially beat a heavily quantized flagship, though this is only a pilot run.
More from coding & agent
- Anydoc: Blazing-Fast Local Document Parsing for AI Agents — devdigest · 2026-08-05
- Steve Yegge's Deep Dive: Agent Swarms That Code All Night and the Future of CI/CD — rseroter · 2026-08-05
- Cloudflare Open Sources Its Agent OS: Workspace, Custom Apps, and Security Framework — michellechen · 2026-08-05
- AI Codex Builds Complex PHP Exploit Chain in Under an Hour, Raising Security Concerns — jedisct1 · 2026-08-05
- Pydantic AI v0.17.0 Released: Introduces Cross-Process Token Budgets — solyarisoftware · 2026-08-05
- Coding Agent Benchmark: Kimi Code Beats Claude Code and Codex — TheZachMueller · 2026-08-05