Agentic coding CLI tools are taking off across AI developer communities, and most now make it effortless to run local
coding models via Ollama or LM Studio. That means your code and data stay private, you can work offline, and you avoid
Even better, today’s small language models (SLMs) are surprisingly capable, often competitive with larger proprietary
assistants on everyday coding tasks, while remaining fast and lightweight on consumer hardware.
In this article, we will review the top five small AI coding models you can run locally. Each integrates smoothly with
popular CLI coding agents and VS Code extensions, so you can add AI assistance to your workflow without sacrificing
gpt-oss-20b is OpenAI’s small-sized open‑weight reasoning and coding model, released under the permissive Apache 2.0
license so developers can run, inspect, and customize it on their own infrastructure.
With 21B parameters and an efficient mixture‑of‑experts architecture, it delivers performance comparable to proprietary
reasoning models like o3‑mini on common coding and reasoning benchmarks, while fitting on consumer GPUs.
Optimized for STEM, coding, and general knowledge, gpt‑oss‑20b is particularly well suited for local IDE assistants,
on‑device agents, and low‑latency tools that need strong reasoning without cloud dependency.
Image from Introducing gpt-oss | OpenAI
Open‑weight license: free to use, modify, and self‑host commercially.
Strong coding & tool use: supports function calling, Python/tool execution, and agentic workflows.
Efficient MoE architecture: 21B total params with only ~3.6B active per token for fast inference.
Long‑context reasoning: native support for up to 128k tokens for large codebases and documents.
Full chain‑of‑thought & structured outputs: emits inspectable reasoning traces and schema‑aligned JSON for robust
# 2. Qwen3-VL-32B-Instruct
Qwen3-VL-32B-Instruct is one of the top open‑source models for coding‑related workflows that also require visual
understanding, making it uniquely useful for developers who work with screenshots, UI flows, diagrams, or code embedded
Built on a 32B multimodal backbone, it combines strong reasoning, clear instruction following, and the ability to
interpret visual content found in real engineering environments. This makes it valuable for tasks like debugging from
screenshots, reading architecture diagrams, extracting code from images, and providing step‑by‑step programming help
Image from Qwen/Qwen3-VL-32B-Instruct
Visual code understanding: understanding UI, code snippets, logs, and errors directly from images or screenshots.
Diagram and UI comprehension: interprets architecture diagrams, flowcharts, and interface layouts for engineering
Strong reasoning for programming tasks: supports detailed explanations, debugging, refactoring, and algorithmic
Instruction‑tuned for developer workflows: handles multi‑turn coding discussions and stepwise guidance.
Open and accessible: fully available on Hugging Face for self‑hosting, fine‑tuning, and integration into developer
# 3. Apriel-1.5-15b-Thinker
Apriel‑1.5‑15B‑Thinker is an open‑weight, reasoning‑centric coding model from ServiceNow‑AI, purpose‑built to tackle
real‑world software‑engineering tasks with transparent “think‑then‑code” behavior.
At 15B parameters, it’s designed to slot into practical dev workflows: IDEs, autonomous code agents, and CI/CD
assistants, where it can read and reason about existing code, propose changes, and explain its decisions in detail.
Its training emphasizes stepwise problem solving and code robustness, making it especially useful for tasks like
implementing new features from natural‑language specs, tracking down subtle bugs across multiple files, and generating
tests and documentation that align with enterprise code standards.
Screenshot from Artificial Analysis
Reasoning‑first coding workflow: explicitly “thinks out loud” before emitting code, improving reliability on complex
Strong multi‑language code generation: writes and edits code in major languages (Python, JavaScript/TypeScript, Java,
etc.) with attention to idioms and style.
Deep codebase understanding: can read larger snippets, trace logic across functions/files, and suggest targeted fixes or
Built‑in debugging and test creation: helps locate bugs, propose minimal patches, and generate unit/integration tests to
Open‑weight & self‑hostable: available on Hugging Face for on‑prem or private‑cloud deployment, fitting into secure
enterprise development environments.
# 4. Seed-OSS-36B-Instruct
Seed‑OSS‑36B‑Instruct is ByteDance‑Seed’s flagship open‑weight language model, engineered for high‑performance coding
and complex reasoning at production scale.
With a robust 36B‑parameter transformer architecture, it delivers strong performance on software‑engineering benchmarks,
generating, explaining, and debugging code across dozens of programming languages while maintaining context over long
The model is instruction‑fine‑tuned to understand developer intent, follow multi‑turn coding tasks, and produce
structured, runnable code with minimal post‑editing, making it ideal for IDE copilots, automated code review, and
agentic programming workflows.
Screenshot from Artificial Analysis
Coding benchmarks: ranks competitively on SciCode, MBPP, and LiveCodeBench, matching or exceeding larger models on
code‑generation accuracy.
Broad language: fluently handles Python, JavaScript/TypeScript, Java, C++, Rust, Go, and popular libraries, adapting to
idiomatic patterns in each ecosystem.
Repository‑level context handling: processes and reasons across multiple files and long codebases, enabling tasks like
bug triage, refactoring, and feature implementation.
Efficient self‑hostable inference: Apache 2.0 license allows deployment on internal infrastructure with optimized
serving for low‑latency developer tools.
Structured reasoning & tool use: can emit chain‑of‑thought traces and integrate with external tools (e.g., linters,
compilers) for reliable, verifiable code generation.
# 5. Qwen3-30B-A3B-Instruct-2507
Qwen3‑30B‑A3B‑Instruct‑2507 is a Mixture-of-Experts (MoE) reasoning model from the Qwen3 family, released in July 2025
and specifically optimized for instruction following and complex software development tasks.
With 30 billion total parameters but only 3 billion active per token, it delivers coding performance competitive with
much larger dense models while maintaining practical inference efficiency.
The model excels at multi-step code reasoning, multi-file program analysis, and tool-augmented development workflows.
Its instruction-tuning enables seamless integration into IDE extensions, autonomous coding agents, and CI/CD pipelines
where transparent, step-by-step reasoning is critical.
Image from Qwen/Qwen3-30B-A3B-Instruct-2507
MoE Efficiency with strong reasoning: 30B total / 3B active parameters per token architecture provides optimal
compute-to-performance ratio for real-time coding assistance.
Native tool & function calling: Built-in support for executing tools, APIs, and functions in coding workflows, enabling
agentic development patterns.
32K token context window: Handles large codebases, multiple source files, and detailed specifications in a single pass
for comprehensive code analysis.
Open weights: Apache 2.0 license allows self-hosting, customization, and enterprise integration without vendor lock-in.
Top performance: Competitive scores on HumanEval, MBPP, LiveCodeBench, and CruxEval, demonstrating robust code
generation and reasoning capabilities
The table below provides a concise comparison of the top local AI coding models, summarizing what each model is best for
and why developers might choose it.
Model Best For Key Strengths & Local Use gpt-oss-20b Fast local coding & reasoning Key strengths: • 21B MoE (3.6B
active) • Strong coding + CoT • 128k context
Why locally: Runs on consumer GPUs • Great for IDE copilots Qwen3-VL-32B-Instruct Coding + visual inputs Key strengths:
• Reads screenshots/diagrams • Strong reasoning • Good instruction following
Why locally: • Ideal for UI/debugging tasks • Multimodal support Apriel-1.5-15B-Thinker Think-then-code workflows Key
strengths: • Clear reasoning steps • Multi-language coding • Bug fixing + test gen
Why locally: • Lightweight + reliable • Great for CI/CD + PR agents Seed-OSS-36B-Instruct High-accuracy repo-level
coding Key strengths: • Strong coding benchmarks • Long-context repo understanding • Structured reasoning
Why locally: • Top accuracy locally • Enterprise-grade Qwen3-30B-A3B-Instruct-2507 Efficient MoE coding & tools Key
strengths: • 30B MoE (3B active) • Tool/function calling • 32k context
Why locally: • Fast + powerful • Great for agentic workflows