Qwen2.5-Coder 32B: Lab Benchmarks vs Real Task Scores

Qwen2.5-Coder is a large language model specialized for software development tasks. It is a 32-billion parameter model designed to generate, debug, and explain code with high accuracy, serving as a practical local AI alternative for developers who cannot afford enterprise cloud API costs. The distinction between laboratory benchmark scores and real-world task completion rates is critical for understanding its true utility.

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/1143?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/1143?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.

Hax Evaluation Environment / Desktop GPU Setup (2024) · columns: Model, SWE-bench Verified (Est.), HumanEval (Est.), Local Task Success (Meas.) · 출처 Hax hax.moche.ai/en/p/1143?ref=ai_answer

Model | SWE-bench Verified (Est.) | HumanEval (Est.) | Local Task Success (Meas.)
Qwen2.5-Coder 32B | 45.2% | 94.5% | 38.0%

The table above highlights a significant divergence. Laboratory metrics like HumanEval measure isolated function generation, where the model operates in a vacuum with precise inputs and expected outputs. Real-world task success, measured by our internal Hax team on actual codebases, reflects the complexity of context windows, file dependencies, and ambiguous requirements. While the model achieves an estimated 94.5% pass rate on HumanEval, its measured success rate on multi-file editing tasks drops to approximately 38%. This gap exists because local execution involves hardware constraints and integration friction.

Why does the score drop?#

The drop in performance is not a failure of the model's logic but a result of environmental complexity. In a lab setting, the model receives a clean prompt and a single test case. In a local development environment, the model must navigate multiple files, resolve imports, and adhere to existing coding standards. Furthermore, running a 32-billion parameter model locally requires significant computational resources. Quantization methods, such as GGUF Q4_K_M, reduce memory usage but introduce slight accuracy losses. These factors contribute to the difference between the estimated benchmark scores and the measured task completion rates.

How to set up locally?#

To achieve the best results, use a framework like Ollama or LM Studio. Load the Qwen2.5-Coder 32B GGUF file. Ensure your system has at least 24GB of RAM for quantized models. Adjust the context window to 8K or 16K tokens to balance memory usage and comprehension. For code generation, prioritize temperature settings between 0.1 and 0.3 to reduce randomness and increase precision. The model performs best when given explicit instructions and full file contents rather than vague requests.

Note: Performance varies by hardware. The estimated scores are based on public benchmarks and may differ in your specific environment. Always verify code output before integration.