Ollama Models for DevOps & Development

ollama_models_compare.md

Ollama Models for DevOps & Development

Comprehensive rating of Ollama models for DevOps tasks, backend development, and frontend development.

Top Tier Models (★★★★★)

Model	Size	DevOps	Backend	Frontend	Best For
deepseek-r1	1.5B-671B	★★★★★	★★★★★	★★★☆☆	Complex reasoning, debugging, infrastructure logic
qwen3-coder	30B-480B	★★★★★	★★★★★	★★★★☆	Agentic coding tasks, long-context code work
devstral-2	123B	★★★★★	★★★★★	★★★★☆	Codebase exploration, multi-file editing, agents

High Performance Models (★★★★☆)

Model	Size	DevOps	Backend	Frontend	Best For
qwen3	0.6B-235B	★★★★☆	★★★★★	★★★★☆	General purpose with thinking + tools
devstral-small-2	24B	★★★★☆	★★★★☆	★★★☆☆	Smaller devstral with vision support
codestral	22B	★★★★☆	★★★★★	★★★★☆	Dedicated code generation
granite4	350M-3B	★★★★☆	★★★★☆	★★★☆☆	Enterprise tool calling, lightweight
qwen2.5-coder	0.5B-32B	★★★★☆	★★★★★	★★★★☆	Code generation/reasoning/fixing
llama3.1	8B-405B	★★★★☆	★★★★☆	★★★☆☆	128K context, general purpose with tools
deepcoder	1.5B-14B	★★★☆☆	★★★★★	★★★★☆	O3-mini level performance, compact

Solid General Purpose (★★★☆☆)

Model	Size	DevOps	Backend	Frontend	Best For
llama4	16x17B-128x17B	★★★☆☆	★★★★☆	★★★★☆	Multimodal, balanced capabilities
mistral-small3.2	24B	★★★☆☆	★★★★☆	★★★★☆	Function calling, vision, instruction following
phi4	14B	★★★☆☆	★★★★☆	★★★☆☆	Strong reasoning and math
gemma3	270M-27B	★★★☆☆	★★★☆☆	★★★★☆	Single GPU friendly, vision support
opencoder	1.5B-8B	★★☆☆☆	★★★★☆	★★★☆☆	Open reproducible code model, bilingual

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Recommendations by Use Case

DevOps & Infrastructure

1. deepseek-r1 (671B) - Best reasoning for complex infrastructure problems
2. devstral-2 (123B) - Excellent for multi-file config management
3. qwen3-coder (480B) - Long context for large infrastructure codebases
4. qwen3 (235B) - Thinking mode helps with troubleshooting

Backend Development

1. qwen3-coder (30B-480B) - Purpose-built for backend coding
2. deepseek-r1 (7B-671B) - Best for complex logic and algorithms
3. codestral (22B) - Specialized code generation
4. qwen2.5-coder (14B-32B) - Strong code fixing and reasoning
5. deepcoder (14B) - Excellent performance for size

Frontend Development

1. qwen2.5-coder (32B) - Good at modern framework code
2. gemma3 (27B) - Vision support helps with UI work
3. mistral-small3.2 (24B) - Vision + function calling
4. devstral-2 (123B) - Multi-file component editing
5. llama4 (multimodal) - Can analyze screenshots/designs

Resource-Constrained Environments

1. granite4 (350M-3B) - Extremely efficient for size
2. deepcoder (1.5B) - Punches above its weight
3. qwen3 (0.6B-1.7B) - Smallest sizes still capable
4. gemma3 (270M-1B) - Lightweight with vision
5. opencoder (1.5B) - Good balance of size/capability

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Important Considerations

Hardware Requirements

• 671B models: Require 8x H100 GPUs or heavy quantization
• 123B-405B models: Need 4-8x high-end GPUs or aggressive quants
• 30B-70B models: 1-2x GPUs with 48GB+ VRAM
• <14B models: Single consumer GPU (RTX 4090, 3090, etc.)
• <3B models: CPU inference viable, edge deployment possible

Feature Flags

• 🔧 Tools: Function calling capability
• 🧠 Thinking: Chain-of-thought reasoning (slower but better)
• 👁️ Vision: Image/screenshot understanding
• ☁️ Cloud: Optimized for cloud API deployment

Trade-offs

• Reasoning models (deepseek-r1, qwen3) add latency but significantly improve complex problem-solving
• Vision models help with UI/screenshot analysis but use more VRAM
• MoE models (mixtral, qwen3-coder) activate fewer parameters per token (faster)
• Tool-enabled models better for multi-step automation and agentic workflows

When NOT to Use These Models

• Simple CRUD operations: Any model works
• Content generation: Not specialized for this
• Pure DevOps scripts: Smaller models (granite4, qwen3 0.6B) sufficient
• Quick CLI tools: Overhead not worth it for trivial tasks

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Quick Selection Guide

Need complex debugging?          → deepseek-r1
Need to explore large codebases? → devstral-2 or qwen3-coder
Need lightweight + capable?      → granite4 or deepcoder
Need vision for UI work?         → gemma3 or mistral-small3.2
Need balanced all-rounder?       → qwen3 or llama3.1
Limited VRAM?                    → granite4, deepcoder, or smollm2

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Last updated: January 2026 Source: ollama.com/library (sorted by newest)