Best Open-Source LLMs for AI Coding Agents: GLM, Qwen, and Local Options

Mar 19, 2026

The Problem: Cost and Privacy Trade-offs

I’ve been using Claude and GPT-4 for my coding agents, but the costs add up fast. A typical agentic session can burn through millions of tokens. More importantly, when I work on proprietary codebases, I hesitate to send everything to a third-party API.

So I started exploring: Can open-source LLMs actually handle coding agent workloads?

The short answer: Yes, but with significant caveats. Let me share what I found.

Why Open-Source for Coding Agents?

Before diving into specific models, let’s understand the motivations:

Concern	Proprietary Models	Open-Source Options
Cost per session	$5-20+ for complex tasks	$0 (local) or ~$0.60/M tokens
Privacy	Code sent to external servers	Full local control
Rate limits	API throttling possible	No limits on local models
Offline use	Requires internet	Works anywhere
Performance	Best-in-class	Gap with top proprietary models

My Exploration: Testing Open-Source Options

I spent several weeks testing different open-source models for coding tasks. Here’s what I discovered through trial and error.

First Attempt: Generic Models

I initially tried general-purpose models like Llama and Mistral. They handled simple coding questions fine, but struggled with:

Understanding complex codebases
Multi-file refactoring
Long context sessions
Following detailed coding conventions

This made sense—coding agents need models specifically trained for code.

Second Attempt: Code-Specialized Models

I then focused on models optimized for coding. Here’s what worked:

Option 1: GLM-4.7-Flash via Ollama (Local)

GLM-4.7-Flash became my go-to for local development. Here’s how to set it up:

# Pull and run the model
ollama run glm-4.7-flash:latest

# Check model status and resource usage
ollama list

The output shows the model details:

NAME                    ID              SIZE      MODIFIED
glm-4.7-flash:latest    abc123def456    26GB      2 hours ago

Hardware Requirements

I tested this on an RTX 5070Ti with 26GB VRAM dedicated to the model. The context window reaches 65K tokens, which is substantial for most coding tasks.

What works well:

Quick code completions
Bug fixes in single files
Generating boilerplate code
Privacy-sensitive projects

Where it struggles:

Long agentic sessions (10+ tool calls)
Complex multi-repository work
Deep debugging requiring sustained context

Option 2: Qwen3.5-397B-A17B (API)

For a cloud-based alternative, Qwen3.5-397B-A17B offers compelling economics. Released in February 2026, it provides:

Metric	Value
Input cost	$0.60 per million tokens
Output cost	$3.60 per million tokens
Best for	Cost-effective cloud access
Recommended	Use U.S.-based providers

# Using OpenAI-compatible API format
curl https://api.example.com/v1/chat/completions \
  -H "Authorization: Bearer $QWEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-397b-a17b",
    "messages": [{"role": "user", "content": "Explain this code"}]
  }'

One important note: Use U.S.-based providers for better latency and reliability. Some international providers have inconsistent availability.

Option 3: GLM 5

Beyond GLM-4.7-Flash, I also tested GLM 5. What struck me was the personality—it feels more conversational and helpful during coding sessions.

Strengths:

Very capable code generation
Pleasant, helpful personality
Good at explaining reasoning

Limitation: Similar to other open-source options, it won’t match Opus on the hardest problems.

The Performance Gap Reality

Let me be direct about something important: Open-source models won’t match Claude Opus on complex agentic work.

Here’s what I mean by “complex agentic work”:

Task Type                    | Opus | GLM-4.7 | Qwen3.5
-----------------------------|------|---------|--------
Simple code completion       | ★★★★★| ★★★★☆   | ★★★★☆
Bug fix in single file       | ★★★★★| ★★★★☆   | ★★★★☆
Multi-file refactoring       | ★★★★★| ★★★☆☆   | ★★★☆☆
Long agentic sessions        | ★★★★★| ★★☆☆☆   | ★★★☆☆
Complex repo work            | ★★★★★| ★★☆☆☆   | ★★★☆☆
Multi-step debugging         | ★★★★★| ★★☆☆☆   | ★★★☆☆

This isn’t a criticism of open-source models—it’s about setting realistic expectations. For many tasks, these models perform admirably. But for the hardest problems, the proprietary models still lead.

When to Choose Each Option

Based on my testing, here’s my decision framework:

Choose GLM-4.7-Flash (Local) When:

Privacy is non-negotiable
You have adequate GPU resources
Working on smaller codebases
Need offline capability
Want zero marginal cost

Choose Qwen3.5 (API) When:

You want low API costs
Can tolerate cloud dependency
Working on moderate complexity tasks
Need larger context without local hardware

Stick with Proprietary Models When:

Complex multi-repository work
Long agentic sessions required
Multi-step debugging tasks
Mission-critical code generation

Practical Tips for Getting Started

If you want to try GLM-4.7-Flash locally:

# 1. Install Ollama (if not already)
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull the model
ollama pull glm-4.7-flash:latest

# 3. Test with a coding question
ollama run glm-4.7-flash:latest "Write a function to reverse a linked list in Python"

# 4. Check resource usage
ollama ps

For Qwen3.5, most providers offer OpenAI-compatible APIs, making integration straightforward.

The Trade-off I’ve Accepted

I’ve settled on a hybrid approach:

Local GLM-4.7-Flash for quick, privacy-sensitive work
Qwen3.5 API for medium-complexity tasks with budget constraints
Claude Opus reserved for complex agentic sessions

This balances cost, privacy, and capability. Your mileage may vary based on your specific needs and hardware.

Conclusion

GLM-4.7-Flash and Qwen3.5 offer viable open-source options for coding agents. The local GLM route gives you privacy and zero marginal cost. The Qwen API gives you cheap cloud access. Both, however, have performance gaps versus Opus on complex tasks—long agentic sessions, complex repo work, and multi-step debugging still favor the best proprietary models.

Choose based on your priorities: privacy, cost, or maximum capability.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Ollama Official Site
👨‍💻 Qwen Model on HuggingFace
👨‍💻 Reddit Discussion: Open-Source Coding LLMs

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!