If you want one clean shortlist for local AI with Ollama, this guide is built for that.
You get:
- A Top 20 overall ranking 🏆
- Different usage categories with separate rankings 🧩
- A graph for each category 📊
Snapshot date: March 27, 2026
Ranking method: practical weighted score (quality + tool use + context window + hardware friendliness + stability in real workflows).
Table of Contents
Top 20 Overall Ranking (Ollama)
| Rank | Model | Best Use | Size Tier | Overall Score (/100) | Why It Ranks Here |
|---|
| 1 | qwen3:30b | General + coding + reasoning | High | 96 | Strong all-around quality with excellent balance for advanced local setups. |
| 2 | deepseek-r1:70b | Hard reasoning, math, logic chains | High | 95 | Very strong reasoning depth for complex step-by-step tasks. |
| 3 | llama4:scout | Multimodal assistants | High | 94 | Strong text + image capability for production multimodal workflows. |
| 4 | qwen3:14b | Daily pro assistant | Mid | 93 | Excellent quality-per-VRAM sweet spot. |
| 5 | gemma3:27b | Vision + multilingual tasks | High | 92 | Great multimodal performance on a single-GPU friendly path. |
| 6 | mistral-small3.1:24b | Fast assistants + function tools | Mid | 91 | Great speed and practical usability for agent-style apps. |
| 7 | qwen3-coder:30b | Coding agents | High | 91 | Purpose-built coding model with long context support. |
| 8 | llama3.3:70b | Reliable chat + enterprise writing | High | 90 | Stable strong baseline for multilingual production use. |
| 9 | devstral:24b | Software engineering agents | Mid | 90 | Strong SWE-oriented behavior and tool use. |
| 10 | deepseek-r1:32b | Reasoning on smaller infra | Mid | 89 | Reasoning-focused option with lower memory needs than 70B. |
| 11 | qwen2.5-coder:32b | Code generation and fixing | High | 88 | Mature coding-focused baseline for local dev workflows. |
| 12 | qwen3:8b | Best compact general model | Mid-Low | 87 | Great quality in a smaller footprint. |
| 13 | gemma3:12b | Vision + compact deployments | Mid | 86 | Strong multimodal quality in an accessible size. |
| 14 | deepseek-r1:14b | Reasoning with moderate VRAM | Mid | 85 | Good logical depth while staying practical for more machines. |
| 15 | mistral:7b | Lightweight fast assistant | Low | 84 | Fast and dependable for everyday interactive workflows. |
| 16 | qwen2.5:14b | General-purpose multilingual | Mid | 84 | Good reliable broad-use model with strong instruction following. |
| 17 | phi4:14b | Tight prompts, compact quality | Mid | 83 | Efficient option for precise response-style workloads. |
| 18 | gemma3:4b | Small multimodal workloads | Low | 82 | Useful vision-capable option for constrained devices. |
| 19 | qwen2.5-coder:14b | Mid-size coding tasks | Mid | 81 | Good coding support where 30B/32B is too heavy. |
| 20 | phi3:3.8b | Ultra-light assistant | Low | 80 | Good starter model for laptops and edge use. |
Category 1: Coding Agents 👨💻
| Rank | Model | Category Score (/100) | Best For |
|---|
| 1 | qwen3-coder:30b | 96 | Full-stack coding agents, multi-file edits |
| 2 | devstral:24b | 94 | SWE bench-style autonomous coding loops |
| 3 | qwen2.5-coder:32b | 92 | Large codebase generation and repair |
| 4 | qwen2.5-coder:14b | 88 | Mid-size local coding workflows |
| 5 | qwen3:14b | 86 | Hybrid coding + product assistant |
Coding Agents Score Graph (Top 5)
qwen3-coder:30b | ############################## 96
devstral:24b | ############################# 94
qwen2.5-coder:32b | ############################ 92
qwen2.5-coder:14b | ########################## 88
qwen3:14b | ######################### 86
Category 2: Reasoning and Math 🧠
| Rank | Model | Category Score (/100) | Best For |
|---|
| 1 | deepseek-r1:70b | 97 | Deep reasoning and long chain-of-thought style tasks |
| 2 | qwen3:30b | 95 | Strong general reasoning with better local practicality |
| 3 | deepseek-r1:32b | 92 | Strong reasoning at lower memory cost |
| 4 | qwen3:14b | 89 | Reasoning-heavy daily production tasks |
| 5 | deepseek-r1:14b | 86 | Budget reasoning workloads |
Reasoning/Math Score Graph (Top 5)
deepseek-r1:70b | ############################## 97
qwen3:30b | ############################# 95
deepseek-r1:32b | ############################ 92
qwen3:14b | ########################### 89
deepseek-r1:14b | ######################### 86
Category 3: Vision and Multimodal 👁️
| Rank | Model | Category Score (/100) | Best For |
|---|
| 1 | llama4:scout | 96 | High-end multimodal copilots |
| 2 | gemma3:27b | 93 | Strong image+text tasks on local infra |
| 3 | mistral-small3.1:24b | 90 | Multimodal assistant with fast responses |
| 4 | gemma3:12b | 87 | Mid-size multimodal apps |
| 5 | gemma3:4b | 82 | Entry-level multimodal workloads |
Vision/Multimodal Score Graph (Top 5)
llama4:scout | ############################## 96
gemma3:27b | ############################ 93
mistral-small3.1 | ########################### 90
gemma3:12b | ######################### 87
gemma3:4b | ###################### 82
Category 4: Multilingual Content 🌍
| Rank | Model | Category Score (/100) | Best For |
|---|
| 1 | qwen3:30b | 95 | Global product content and translation |
| 2 | llama3.3:70b | 93 | Reliable multilingual customer-facing assistants |
| 3 | gemma3:27b | 91 | Multilingual + vision use cases |
| 4 | qwen2.5:14b | 87 | Practical multilingual deployment |
| 5 | qwen3:8b | 84 | Compact multilingual assistant |
Multilingual Score Graph (Top 5)
qwen3:30b | ############################# 95
llama3.3:70b | ############################ 93
gemma3:27b | ########################### 91
qwen2.5:14b | ######################### 87
qwen3:8b | ####################### 84
Category 5: Low Resource / Edge Devices ⚡
| Rank | Model | Category Score (/100) | Best For |
|---|
| 1 | qwen3:8b | 90 | Best compact quality for edge deployment |
| 2 | gemma3:4b | 88 | Small multimodal apps on limited hardware |
| 3 | phi4:14b | 86 | Efficient quality where memory is capped |
| 4 | mistral:7b | 85 | Fast local chat and utility tasks |
| 5 | phi3:3.8b | 83 | Ultra-light baseline assistants |
Low Resource Score Graph (Top 5)
qwen3:8b | ########################### 90
gemma3:4b | ########################## 88
phi4:14b | ######################### 86
mistral:7b | ######################## 85
phi3:3.8b | ####################### 83
Category 6: Long Context Workloads 📚
| Rank | Model | Category Score (/100) | Context Strength |
|---|
| 1 | llama4:scout | 97 | Very large context-oriented architecture |
| 2 | qwen3:30b | 95 | Strong long-document and retrieval workflows |
| 3 | qwen3-coder:30b | 93 | Large codebase and repo-level operations |
| 4 | mistral-small3.1:24b | 90 | 128K-oriented practical pipelines |
| 5 | deepseek-r1:32b | 88 | Long reasoning sessions with fewer resets |
Long Context Score Graph (Top 5)
llama4:scout | ############################## 97
qwen3:30b | ############################# 95
qwen3-coder:30b | ############################ 93
mistral-small3.1 | ########################### 90
deepseek-r1:32b | ########################## 88
Quick Recommendations by Hardware
- 💻 Laptop / low VRAM:
qwen3:8b, gemma3:4b, phi3:3.8b
- 🖥️ Single strong GPU / unified memory Mac:
qwen3:14b, mistral-small3.1:24b, devstral:24b
- 🧰 Workstation / multi-GPU:
qwen3:30b, deepseek-r1:70b, llama4:scout
References
