slee-persis

Gvs5h — Development skill for Claude Code

Development community

GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard Fable 5 Level Coding for a Fifth the Price - or on a Single GPU.

How to install Gvs5h

This entry records only its repository, not the path inside it, so there is no exact command to give. Open slee-persis/GVS5H and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Gvs5h does

GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard Fable 5 Level Coding for a Fifth the Price - or on a Single GPU.

Alternatives in Development

Dao Code — Open-source TypeScript terminal coding agent for DeepSeek-V4 — builds on DeepSeek's strong price-performance a 1.3k ★
Pool Ohlcv — Fetch price and volume history for a specific Meteora pool 722 ★
Fable OS — An agentic operating system where the kernel is controlled directly by Claude 324 ★

README

GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard

Results

![Manager vs single call, four models — LCB-100, 5 passes, 128k max tokens, reasoning ON](assets/manager_vs_single_call_four_models.png)

![What one pass costs — LCB-100, 5 passes, single call vs manager, against Fable 5](assets/what_one_pass_costs.png)

**Abstract.** Frontier coding performance is typically bought with larger proprietary models at high cost. We introduce ledger-based zero-shot self-orchestration, a training-free method in which fresh instances of one model decompose problems and coordinate through a shared filesystem holding a plan, notes and current solution. Across nine open and closed-weight models on the 100 latest *hard* LiveCodeBench problems, the method yields gains of up to 23.2 percentage points on pinned backends and offers two routes to frontier-level accuracy. Orchestrated GPT-5.6-Terra reaches 88.0% pass@1 against Fable 5's 90.4% at 19% of the cost, and locally served, open-weight Qwen3.8-27B rises from 69.2% to 92.4%, slightly exceeding Fable 5. Gains are not universal: some models are unchanged or worse. Transcript analysis attributes the gain to decomposition and persistent context. Inference-time organization can approach frontier coding accuracy at a fraction of the cost, or slightly exceed it on self-hostable weights.

— [the paper](paper/iclr2027_conference.pdf)

Running the code

Needs [uv](https://docs.astral.sh/uv/) and an API key for the model you want to test.

cd codebase/v2-current
export OPENAI_API_KEY=...

LCB_RELEASE=release_v6 \
ESCALATION_CLOUD_MAX_TOKENS=128000 \
ESCALATION_CLOUD_TIMEOUT=7200 \
MULTIAGENT_MODEL=openai:gpt-5.6-terra \
uv run --no-project --python 3.12 --with 'datasets<4' --with numpy --with anthropic \
  python escalation/run_bench.py --engine multiagent --only lcb --lcb 100 --parallel 8

--engine multiagent runs the manager; --engine single is the one-call baseline.
Other models: `ant