Artificial Intelligence (AI) Models

Model and Use Cases

Model Name	Use Cases	Strengths	Notes
Codebooga	Programming	Python and JavaScript	1
CodeGeeX	Programming	Cross language translation, plugins for many IDEs	2
Codeqwen	Programming		3
Deepseek-R1	General	Reasoning
Dolphin-mistral	General	Uncensored, use when other models refuse answers	4
DolphinMixtral	Programming	Uncensored	5
Gemma2	General	2B good for low hardware	6
Gemma3	General, RAG	low hardware/1 GPU, context length, multilingual, multimodal
GPT-oss-20b	General, Reasoning, Agentic	Configurable reasoning
Granite	General	FOSS, SLM, 4 series low to medium hardware, multi modal
Kimi-K2-Instruct	General, Agentic	Chat, agentic
Llama3	General		7
Meditron	Medical	Medical questions, diagnosis, information
Medllama2	Medical	Medical questions, trained on open source medical data	8
MedGemma Series	Medical	Medical text and image comprehension	9
MedImageInsight	Medical	medical image embeddings (radiology, pathology, etc.)	10
MedImageParse	Medical	image segmentation	11
CXRReportGen	Medical	chest X-ray report generation	12
Mistral	General, Programming	7B ok for low hardware	13
Moondream	Vision	Small for edge devices
Nemotron-mini	Role-play, RAG, Function	4b for low hardware	14
Phi3	General, RAG	low hardware, context length
phi-4	General	low hardware, reasoning
Phi4-mini	General, RAG	low hardware, multilingual, context length
Qwen3 series	General	Multiple models depending on use
Qwen-Image	Image generation, editing	Good at text, especially Chinese
StarCoder	Programming	Trained on 80+ languages, Small to large models	15
WizardCoder	Programming		16
Zephyr	Assistant	Trained version of Mistral and Mixtral as help assistant	17

multimodal means the model can do text and image

Footnotes and Sources

Model and RAM Recommendations

Model Size	RAM
7B	8 GB
13B	16 GB
33B	32 GB

From Ollama README Guidance ¹⁸

Models for Programming, Computer Language Development and Use Cases

Summary

Source: Choosing the right model in GitHub Copilot - Microsoft Tech Community

1. General Development Tasks

   New functions, creating tests/documentation, improving code

   • GPT-4.1
   • GPT-5-mini
   • Claude Sonnet
   • Big Pickle (OpenCode Zen)

2. Light Tasks

   Quick explanations, JSON/YAML transformations, small refactors, regex creation, short Q&A

   • Claude Haiku 4.5
   • MiMo V2 Omni (multi-modal)
   • Nemotron 3 Super Free (text only)

3. Complex Debugging, Deep Reasoning

   Analyzing code, debugging hard issues, architecture decisions, multi-step reasoning, performance analysis

   • GPT-5-MINI
   • GPT-5.1
   • GPT-5.2
   • Claude Opus

4. Multi-step Agentic Development

   Entire repository changes, migrations, creating features, multiple file planning, automated workflows (plan > run > change)

   • GPT-5.1-Codex-Max
   • GPT-5.2-Codex
   • MiMo V2 Pro

Specific Use Cases

Source: FY26—Advanced-GitHub-Copilot-Workshop/08-models-context/docs - GitHub with some personal notes

Task	Recommended Model	Why
General completion, quick question	GPT-4o / GPT-4.1	Free, general purpose
Refactoring a single file	Claude Haiku 4.5 (0.33×)	Fast, inexpensive for focused edits
Multi-file feature implementation	Claude Sonnet 4.6 (1×)	Follows instructions
Analyse 100k+ token codebase	Gemini 3.1 Pro (1×)	Large 1M context
Architecture design	Claude Opus 4.6 (3×)	Reasoning
Test generation	Claude Sonnet 4.6 (1×)	Reliable naming + Arrange/Act/Assert structure
SQL query generation	GPT-4o (0×) or Sonnet	Both handle SQL well; 0× for routine queries
Legacy code explanation	Claude Sonnet 4.6 (1×)	Superior contextual narrative explanation
Change, pull request summary	GPT-5 mini (0×)	Fast, accurate, free
Documentation	GPT-5 mini (0×)	Fast, accurate, free

Older Models

Model Name	Use Cases	Strengths	Notes
Qwen2.5-Instruct	General	long context tasks due to 1 million token context window
Qwen2.5	General	3b for low hardware	19
Qwen2	General, Programming	Chat, Small to Large models	20

See also

• Deepseek R1 Locally, Open-Source Ai Tools, ollama, automation, RAG - Deepseek R1 Locally, Open-Source Ai Tools, ollama, automation, RAG

Resources

• Ollama Models Library

Footnotes

1. Coding LLMs Copilot Alternatives ↩

2. Coding LLMs Copilot Alternatives ↩

3. Local LLMs on Linux with Ollama ↩

4. Local LLMs on Linux with Ollama ↩

5. Coding LLMs Copilot Alternatives ↩

6. I Ran 9 Popular LLMs on Raspberry Pi 5; Here’s What I Found ↩

7. Local LLMs on Linux with Ollama ↩

8. 5 easy ways to run an LLM locally ↩

9. MedGemma supports image understanding across radiology, pathology, dermatology, and others. Available on Google Vertex AI and MedGemma - Ollama ↩

10. Healthcare AI foundation models (classic) - Microsoft Foundry (classic) portal | Microsoft Learn ↩

11. Healthcare AI foundation models (classic) - Microsoft Foundry (classic) portal | Microsoft Learn ↩

12. Healthcare AI foundation models (classic) - Microsoft Foundry (classic) portal | Microsoft Learn ↩

13. Coding LLMs Copilot Alternatives ↩

14. I Ran 9 Popular LLMs on Raspberry Pi 5; Here’s What I Found ↩

15. Tabby ML Windows Install ↩

16. Coding LLMs Copilot Alternatives ↩

17. 5 easy ways to run an LLM locally ↩

18. Ollama README Guidance on models and RAM ↩

19. I Ran 9 Popular LLMs on Raspberry Pi 5; Here’s What I Found ↩

20. Tabby ML Windows Install ↩