--- title: "OpenCoder-llm vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opencoder-llm-opencoder-llm-vs-shishirpatil-gorilla" tools: ["opencoder-llm-opencoder-llm", "shishirpatil-gorilla"] --- # OpenCoder-llm vs gorilla *GraphCanon updated Aug 8, 2026* ## Verdict Pick OpenCoder-llm if openCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [OpenCoder-llm](https://opencoder-llm.github.io/) reports 2.1k GitHub stars, 125 forks, and 11 open issues, last pushed Dec 8, 2024. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 272 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [OpenCoder-llm's repository](https://github.com/OpenCoder-llm/OpenCoder-llm) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | The Open Cookbook for Top-Tier Code Large Language Models | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 2,103 | 12,988 | | Forks | 125 | 1,397 | | Open issues | 11 | 272 | | Language | Python | Python | | Adopt for | OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | MIT | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 604d | 117d | | Open issues (now) | 11 | 272 | | Full report | [trust report](/tools/opencoder-llm-opencoder-llm/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: OpenCoder-llm - **Adopt for:** OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. ## 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 OpenCoder-llm if… - License: OpenCoder-llm is MIT, gorilla is Apache-2.0. - Tags unique to OpenCoder-llm: code generation, data filtering, dataset, evaluation-framework. - Also covers Data & Retrieval, LLM Frameworks. - When you need access to both English and Chinese language support in your code generation tasks. ### Choose gorilla if… - License: gorilla is Apache-2.0, OpenCoder-llm is MIT. - 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. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ## When NOT to use OpenCoder-llm - If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. - For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. - If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. - When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks. ## 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 OpenCoder-llm and gorilla? OpenCoder-llm: The Open Cookbook for Top-Tier Code Large 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 OpenCoder-llm over gorilla? Choose OpenCoder-llm over gorilla when License: OpenCoder-llm is MIT, gorilla is Apache-2.0; Tags unique to OpenCoder-llm: code generation, data filtering, dataset, evaluation-framework; Also covers Data & Retrieval, LLM Frameworks; When you need access to both English and Chinese language support in your code generation tasks. ### When should I choose gorilla over OpenCoder-llm? Choose gorilla over OpenCoder-llm when License: gorilla is Apache-2.0, OpenCoder-llm is MIT; 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; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### When should I avoid OpenCoder-llm? If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks. ### 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 OpenCoder-llm or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 2,103). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenCoder-llm and gorilla open source? Yes - both are open-source projects on GitHub (OpenCoder-llm: MIT, gorilla: Apache-2.0). ### Where can I find alternatives to OpenCoder-llm or gorilla? GraphCanon lists graph-backed alternatives at [OpenCoder-llm alternatives](/tools/opencoder-llm-opencoder-llm/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([OpenCoder-llm markdown twin](/tools/opencoder-llm-opencoder-llm/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/opencoder-llm-opencoder-llm-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenCoder-llm or gorilla? OpenCoder-llm: 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 OpenCoder-llm and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenCoder-llm trust report](/tools/opencoder-llm-opencoder-llm/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opencoder-llm-opencoder-llm`](/api/graphcanon/graph?tool=opencoder-llm-opencoder-llm) - 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/_