--- title: "bigcode-evaluation-harness vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-shishirpatil-gorilla" tools: ["bigcode-project-bigcode-evaluation-harness", "shishirpatil-gorilla"] --- # bigcode-evaluation-harness vs gorilla *GraphCanon updated Oct 6, 2026* ## 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 gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 262 forks, and 99 open issues, last pushed Jul 22, 2025. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 278 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 1,062 | 13,017 | | Forks | 262 | 1,406 | | Open issues | 99 | 278 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 440d | 147d | | Open issues (now) | 99 | 278 | | Stars delta | +4 (30d) | +29 (30d) | | Open issues delta | +3 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: gorilla - **Pricing:** freemium - **Requirements:** Gorilla works best with Python environments and requires installation through pip or local repository cloning. - **Adopt for:** Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. - **License detail:** Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. ## Choose when ### 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, code-generation, docker, python. - 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. ### Choose gorilla if… - Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.. - Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api. - Also covers Model Training. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ## 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. ## When NOT to use gorilla - Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. - If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ## Common questions ### What is the difference between bigcode-evaluation-harness and gorilla? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over gorilla? Choose bigcode-evaluation-harness over gorilla 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, code-generation, docker, python; 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 gorilla over bigcode-evaluation-harness? Choose gorilla over bigcode-evaluation-harness when Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api; Also covers Model Training; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### 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 gorilla? Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ### Is bigcode-evaluation-harness or gorilla more popular on GitHub? gorilla has more GitHub stars (13,017 vs 1,062). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and gorilla open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to bigcode-evaluation-harness or gorilla? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [gorilla markdown twin](/tools/shishirpatil-gorilla/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/bigcode-project-bigcode-evaluation-harness-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or gorilla? bigcode-evaluation-harness: Dormant. gorilla: Slowing. 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 gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - 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/_