--- title: "FullStackBench vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bytedance-fullstackbench-vs-declare-lab-instruct-eval" tools: ["bytedance-fullstackbench", "declare-lab-instruct-eval"] --- # FullStackBench vs instruct-eval *GraphCanon updated Aug 7, 2026* ## Verdict Pick FullStackBench if fullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples; pick instruct-eval if key facts about instruct-eval. [FullStackBench](https://github.com/bytedance/FullStackBench) reports 121 GitHub stars, 10 forks, and 1 open issues, last pushed May 7, 2025. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [FullStackBench's repository](https://github.com/bytedance/FullStackBench) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [FullStackBench](/tools/bytedance-fullstackbench.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | Multilingual benchmark for evaluating LLMs in full-stack coding | Quantitative evaluation for instruction-tuned language models | | Stars | 121 | 552 | | Forks | 10 | 45 | | Open issues | 1 | 24 | | Language | Python | Python | | Adopt for | FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples. | Key facts about instruct-eval | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The tool is distributed under Apache-2.0 license | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FullStackBench](/tools/bytedance-fullstackbench.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Days since push | 455d | 879d | | Open issues (now) | 1 | 24 | | Full report | [trust report](/tools/bytedance-fullstackbench/trust.md) | [trust report](/tools/declare-lab-instruct-eval/trust.md) | ## Decision facts: FullStackBench - **Adopt for:** FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples. ## Decision facts: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Choose when ### Choose FullStackBench if… - Tags unique to FullStackBench: benchmarks, full stack coding, llm-evaluation. - When you need to assess LLM performance in full-stack programming tasks covering multiple domains and languages - More recently updated (last pushed May 7, 2025). ### Choose instruct-eval if… - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ## When NOT to use FullStackBench - Avoid if testing scope is limited to a single or few programming languages as FullStackBench covers a wide range of languages - Not suitable if your focus is solely on theoretical coding challenges instead of practical, full-stack tasks ## When NOT to use instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## Common questions ### What is the difference between FullStackBench and instruct-eval? FullStackBench: Multilingual benchmark for evaluating LLMs in full-stack coding. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose FullStackBench over instruct-eval? Choose FullStackBench over instruct-eval when Tags unique to FullStackBench: benchmarks, full stack coding, llm-evaluation; When you need to assess LLM performance in full-stack programming tasks covering multiple domains and languages; More recently updated (last pushed May 7, 2025). ### When should I choose instruct-eval over FullStackBench? Choose instruct-eval over FullStackBench when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### When should I avoid FullStackBench? Avoid if testing scope is limited to a single or few programming languages as FullStackBench covers a wide range of languages Not suitable if your focus is solely on theoretical coding challenges instead of practical, full-stack tasks ### When should I avoid instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### Is FullStackBench or instruct-eval more popular on GitHub? instruct-eval has more GitHub stars (552 vs 121). Stars measure visibility, not whether either tool fits your constraints. ### Are FullStackBench and instruct-eval open source? Yes - both are open-source projects on GitHub (FullStackBench: Apache-2.0, instruct-eval: Apache-2.0). ### Where can I find alternatives to FullStackBench or instruct-eval? GraphCanon lists graph-backed alternatives at [FullStackBench alternatives](/tools/bytedance-fullstackbench/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([FullStackBench markdown twin](/tools/bytedance-fullstackbench/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/bytedance-fullstackbench-vs-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FullStackBench or instruct-eval? FullStackBench: Dormant. instruct-eval: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for FullStackBench and instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FullStackBench trust report](/tools/bytedance-fullstackbench/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bytedance-fullstackbench`](/api/graphcanon/graph?tool=bytedance-fullstackbench) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_