Comparison
bigcode-evaluation-harness vs CodeGeeX
Verdict
Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick CodeGeeX if codeGeeX is an open-source multilingual code generation model that supports both Mindspore and PyTorch frameworks, making it suitable for developers working with Ascend 910 AI Processors or GPU platforms.
Markdown twin · bigcode-evaluation-harness alternatives · CodeGeeX alternatives
GraphCanon updated Oct 6, 2026
vs
Trust & integrity
| Signal | bigcode-evaluation-harness | CodeGeeX |
|---|---|---|
| Maintenance | Dormant (440d since push) As of Oct 6, 2026 · github_public_v1 | Dormant (766d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 6, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · 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
- bigcode-evaluation-harness
- A framework for evaluating autoregressive code generation language models.
- CodeGeeX
- CodeGeeX: An Open Multilingual Code Generation Model
Stars
- bigcode-evaluation-harness
- 1.1k
- CodeGeeX
- 8.8k
Forks
- bigcode-evaluation-harness
- 262
- CodeGeeX
- 686
Open issues
- bigcode-evaluation-harness
- 99
- CodeGeeX
- 189
Language
- bigcode-evaluation-harness
- Python
- CodeGeeX
- Python
Adopt for
- bigcode-evaluation-harness
- bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments.
- CodeGeeX
- CodeGeeX is an open-source multilingual code generation model that supports both Mindspore and PyTorch frameworks, making it suitable for developers working with Ascend 910 AI Processors or GPU platforms.
Persona
- bigcode-evaluation-harness
- -
- CodeGeeX
- -
Runtime
- bigcode-evaluation-harness
- -
- CodeGeeX
- -
License
- bigcode-evaluation-harness
- bigcode-evaluation-harness is distributed under the Apache-2.0 license.
- CodeGeeX
- CodeGeeX is licensed under the Apache-2.0 license, allowing for broad use, modification, and distribution of the code, while the model itself is under a separate license.
Last pushed
- bigcode-evaluation-harness
- Jul 22, 2025
- CodeGeeX
- Aug 13, 2024
Categories
- bigcode-evaluation-harness
- Evaluation & Observability
- CodeGeeX
- LLM Frameworks, Model Training
Trust and health
Days since push
- bigcode-evaluation-harness
- 440d
- CodeGeeX
- 766d
Open issues (now)
- bigcode-evaluation-harness
- 99
- CodeGeeX
- 189
Stars delta
- bigcode-evaluation-harness
- +4 (30d)
- CodeGeeX
- -3 (30d)
Open issues delta
- bigcode-evaluation-harness
- +3 (30d)
- CodeGeeX
- +1 (30d)
Full report
- bigcode-evaluation-harness
- Trust report
- CodeGeeX
- Trust report
Choose bigcode-evaluation-harness if…
- Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation..
- Tags unique to bigcode-evaluation-harness: autoregressive-models, docker, python.
- Also covers Evaluation & Observability.
- bigcode-evaluation-harness ships Docker support for self-hosted deployment.
- When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
When NOT to use bigcode-evaluation-harness
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker.
- If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
Choose CodeGeeX if…
- Requirements: Requires Docker; Requires Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+.; Supports installation via pip or through a Docker container for quick setup..
- Tags unique to CodeGeeX: code-generation-model, gpu-support, mindspore, multilingual.
- Also covers LLM Frameworks, Model Training.
- When you need a multilingual code generation model that supports both Mindspore and PyTorch frameworks, allowing flexibility between Ascend 910 AI Processors and GPU platforms.
When NOT to use CodeGeeX
- If your project is strictly limited to TensorFlow or other frameworks not supported by CodeGeeX, as it primarily supports Mindspore and PyTorch.
- When your development environment does not support Python 3.7+, CUDA 11+, PyTorch 1.10+, or DeepSpeed 0.6+, as these are the minimum requirements for running CodeGeeX.
- If you are looking for a model that does not require specific hardware like Ascend 910 AI Processors or GPUs, as CodeGeeX is optimized for these platforms.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bigcode-project/bigcode-evaluation-harness) · observed Oct 6, 2026
- GitHub forks (bigcode-project/bigcode-evaluation-harness) · observed Oct 6, 2026
- Last push (bigcode-project/bigcode-evaluation-harness) · observed Jul 22, 2025
- License file (Apache-2.0) · observed Oct 6, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zai-org/CodeGeeX) · observed Sep 20, 2026
- GitHub forks (zai-org/CodeGeeX) · observed Sep 20, 2026
- Last push (zai-org/CodeGeeX) · observed Aug 13, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: bigcode-evaluation-harness 1.1k · CodeGeeX 8.8k (synced Oct 6, 2026).
Common questions
- What is the difference between bigcode-evaluation-harness and CodeGeeX?
- bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. CodeGeeX: CodeGeeX: An Open Multilingual Code Generation Model. See the comparison table for live GitHub stats and shared categories.
- When should I choose bigcode-evaluation-harness over CodeGeeX?
- Choose bigcode-evaluation-harness over CodeGeeX when Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive-models, docker, python; Also covers Evaluation & Observability; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
- When should I choose CodeGeeX over bigcode-evaluation-harness?
- Choose CodeGeeX over bigcode-evaluation-harness when Requirements: Requires Docker; Requires Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+.; Supports installation via pip or through a Docker container for quick setup.; Tags unique to CodeGeeX: code-generation-model, gpu-support, mindspore, multilingual; Also covers LLM Frameworks, Model Training; When you need a multilingual code generation model that supports both Mindspore and PyTorch frameworks, allowing flexibility between Ascend 910 AI Processors and GPU platforms.
- When should I avoid bigcode-evaluation-harness?
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
- When should I avoid CodeGeeX?
- If your project is strictly limited to TensorFlow or other frameworks not supported by CodeGeeX, as it primarily supports Mindspore and PyTorch. When your development environment does not support Python 3.7+, CUDA 11+, PyTorch 1.10+, or DeepSpeed 0.6+, as these are the minimum requirements for running CodeGeeX. If you are looking for a model that does not require specific hardware like Ascend 910 AI Processors or GPUs, as CodeGeeX is optimized for these platforms.
- Is bigcode-evaluation-harness or CodeGeeX more popular on GitHub?
- CodeGeeX has more GitHub stars (8,806 vs 1,062). Stars measure visibility, not whether either tool fits your constraints.
- Are bigcode-evaluation-harness and CodeGeeX open source?
- Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, CodeGeeX: Apache-2.0).
- Where can I find alternatives to bigcode-evaluation-harness or CodeGeeX?
- GraphCanon lists graph-backed alternatives at bigcode-evaluation-harness alternatives and CodeGeeX alternatives (bigcode-evaluation-harness markdown twin, CodeGeeX 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, bigcode-evaluation-harness or CodeGeeX?
- bigcode-evaluation-harness: Dormant. CodeGeeX: 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 bigcode-evaluation-harness and CodeGeeX?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: bigcode-evaluation-harness trust report; CodeGeeX trust report.