Comparison
FullStackBench vs instruct-eval
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.
Markdown twin · FullStackBench alternatives · instruct-eval alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | FullStackBench | instruct-eval |
|---|---|---|
| Maintenance | Dormant (455d since push) As of 2w · github_public_v1 | Dormant (879d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- FullStackBench
- Multilingual benchmark for evaluating LLMs in full-stack coding
- instruct-eval
- Quantitative evaluation for instruction-tuned language models
Stars
- FullStackBench
- 121
- instruct-eval
- 552
Forks
- FullStackBench
- 10
- instruct-eval
- 45
Open issues
- FullStackBench
- 1
- instruct-eval
- 24
Language
- FullStackBench
- Python
- instruct-eval
- Python
Adopt for
- FullStackBench
- FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples.
- instruct-eval
- Key facts about instruct-eval
Persona
- FullStackBench
- -
- instruct-eval
- -
Runtime
- FullStackBench
- -
- instruct-eval
- -
License
- FullStackBench
- Apache-2.0
- instruct-eval
- The tool is distributed under Apache-2.0 license
Last pushed
- FullStackBench
- May 7, 2025
- instruct-eval
- Mar 10, 2024
Categories
- FullStackBench
- Evaluation & Observability
- instruct-eval
- Evaluation & Observability
Trust and health
Days since push
- FullStackBench
- 455d
- instruct-eval
- 879d
Open issues (now)
- FullStackBench
- 1
- instruct-eval
- 24
OSV dependency advisories
- FullStackBench
- No published findings from this source as of 2026-07-11
- instruct-eval
- Published findings
Full report
- FullStackBench
- Trust report
- instruct-eval
- Trust report
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).
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bytedance/FullStackBench) · observed Aug 5, 2026
- GitHub forks (bytedance/FullStackBench) · observed Aug 5, 2026
- Last push (bytedance/FullStackBench) · observed May 7, 2025
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (declare-lab/instruct-eval) · observed Aug 7, 2026
- GitHub forks (declare-lab/instruct-eval) · observed Aug 7, 2026
- Last push (declare-lab/instruct-eval) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FullStackBench 121 · instruct-eval 552 (synced Aug 5, 2026).
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 and instruct-eval alternatives (FullStackBench markdown twin, instruct-eval markdown twin), 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 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; instruct-eval trust report.