Qwen2.5-Coder 30B Local Agent Benchmarks and Draft Quality

Qwen2.5-Coder 30B is a large language model optimized for code generation and understanding, designed to function as a local coding agent for software development tasks. It serves as the current state-of-the-art reference for on-premise AI coding assistants, succeeding the Qwen2-Coder series with enhanced instruction following and tool-use capabilities. The model addresses the need for privacy-preserving, low-latency code assistance without reliance on external cloud APIs. Local deployment allows developers to iterate on code drafts and reviews internally, reducing data leakage risks. However, performance varies significantly based on hardware acceleration and prompt engineering strategies. The 30B parameter size represents a balance between computational cost and logical reasoning capacity, suitable for consumer-grade GPUs with sufficient VRAM.

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1144?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1144?ref=ai_answer

Dataset item | Measured value | Date | Source
발행 성공률 | 100.0 % | 2026-07-03 | Hax 운영 실측(telemetry/funnel)
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
생성 큐 성공률(누적 143건) | 77.6 % | 2026-06-30 | Hax ComfyUI 풀 운영 통계

측정 방법론 · bench_harness.probe_unified_latency +1 more

표본

2 measured metrics (Hax /data curated)

수집일

2026-06-30 ~ 2026-07-03

방법

bench_harness.probe_unified_latency; 누적 143건 중 성공 111(취소 21; 실패 11)

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Qwen2.5-Coder 30B Performance Estimates vs. Cloud Models · columns: Metric, Qwen2.5-Coder 30B (Local), GPT-4o (Cloud Reference), Claude 3.5 Sonnet (Cloud Reference) · 출처 Hax hax.moche.ai/en/p/1144?ref=ai_answer

Metric | Qwen2.5-Coder 30B (Local) | GPT-4o (Cloud Reference) | Claude 3.5 Sonnet (Cloud Reference)
Edit Success Rate | 추정 58% | 추정 72% | 추정 75%
Compile Pass Rate | 추정 45% | 추정 60% | 추정 62%
Draft Review Time | 추정 120s/1k LOC | 추정 15s/1k LOC | 추정 18s/1k LOC
Hax Status | 측정대기 (Not Measured) | N/A | N/A

The following diagram illustrates the latency profile of local inference compared to cloud alternatives. Local models exhibit higher initial latency due to context loading but offer consistent token generation times once warmed up. Cloud models benefit from massive parallel processing but suffer from network jitter and API rate limits.

How does Qwen2.5-Coder 30B perform in code edit tasks?#

Code edit success rates measure the model's ability to apply specific modifications to existing codebases without introducing syntax errors or logical bugs. Estimates suggest that Qwen2.5-Coder 30B achieves a success rate of approximately 58% for complex refactoring tasks. This metric depends heavily on the clarity of the prompt and the context window provided. Local agents often struggle with large-context coherence compared to cloud models with optimized retrieval mechanisms. Developers must curate context snippets carefully to maximize performance. The model excels in Python and JavaScript but may show reduced accuracy in less common languages.

The diagram below depicts the workflow of a local coding agent, highlighting the iterative feedback loop between generation and validation.

What is the compile pass rate for generated code?#

Compile pass rate indicates whether the generated code executes without immediate syntax or compilation errors. Estimates place Qwen2.5-Coder 30B at around 45% for new file generation in typed languages like TypeScript or Go. This rate improves with few-shot prompting and strict schema definitions. Local models lack the real-time feedback loops of integrated development environments (IDEs) that cloud models might simulate. Developers should implement automated testing pipelines to filter invalid outputs. The cost of local compute is justified by the control over the development environment and data privacy.

The following chart compares estimated draft review times. Local inference is slower per token but offers uninterrupted access without network dependency.

Note: Performance metrics are estimates based on community benchmarks and may vary with hardware configuration. Always validate critical code manually.