DeepSeek Paved the Way, Qwen 2.5 Max Is Leading the Charge

Last week, while our AI team was knee-deep in building pipelines on DeepSeek, the news of Qwen 2.5 Max dropped. And let’s just say, it wasn’t just another headline—it was a moment that made us pause and think: What does this mean for us?

Turns out, it means a LOT.

The Cost Factor: Why Affordable Models Matter

Let’s start with the obvious: cost. DeepSeek and Qwen 2.5 Max are not just smart—they’re affordable. DeepSeek’s $1-per-million-tokens pricing was already a game-changer, but Qwen 2.5 Max takes it a step further. With 325 billion parameters (compared to GPT’s trillion-plus), it’s leaner, more efficient, and doesn’t require the same infrastructure muscle.

But why does this matter to us? Because agentic frameworks—the backbone of what we’re building at Quash—can get really expensive, really fast.

And another thing about agents: they’re not static. They’re constantly communicating, iterating, and evolving. Every interaction between agents adds to the token count, and every token adds to the cost. What starts as a 100-token input can quickly swell up to 300, 500, or even 1,000 tokens as the conversation grows. And if you’re not careful, those costs can spiral out of control.

But with models like DeepSeek and Qwen 2.5 Max, we’re not just getting affordability—we’re getting smarter agents. These models are designed to avoid unnecessary back-and-forth, cutting down on those infinite loops that can break the bank. For us, that means we can have longer, more complex conversations with our agents without worrying about the financial fallout.

The Smartness Factor: Why Smarter Agents Are Non-Negotiable

Let’s be clear: affordability alone isn’t enough. We need agents that are smart. Agents that can understand tasks, interpret context, and deliver results without getting stuck in endless loops.

Take some of the complex tasks we’re tackling at Quash—like crawling knowledge graphs or writing test scripts. These aren’t one-and-done tasks. They require extended conversations with agents, where every input builds on the last. And that’s where smarter models like Qwen 2.5 Max shine.

With their Mixture-of-Experts (MoE) architecture, these models activate only the most relevant parts for a given task, making them both efficient and effective. It’s like having a team of specialists on standby, ready to jump in when needed.

The Open-Source Advantage: Why It’s a Game-Changer

For us, this is the most exciting part: open-source models. DeepSeek is already on Hugging Face, and Qwen’s base model is already testament to how powerful open-source can be.

Why does this matter? Because it gives us the freedom to fine-tune these models for our specific use cases. Unlike proprietary models like GPT-4o, which lock you into their ecosystem, open-source models let us take control. We can deploy them on our own servers, tweak them to our needs, and build agents that are tailor-made for Quash.

Imagine this: tomorrow, we want to build a super-smart LLM that can fix bugs or automate complex testing workflows. With open-source models like Qwen 2.5 Max, we’re not starting from scratch. We’re building on a foundation that’s already smart, efficient, and cost-effective.

What This Means for Quash

So, what does all this mean for us? In short, it’s a game-changer.

Longer Conversations: We can now have extended, meaningful interactions with our agents without worrying about cost.
Complex Task Solving: Smarter models mean we can tackle more complex tasks, from crawling knowledge graphs to writing intricate test scripts.
Customization: Open-source models give us the flexibility to fine-tune agents for our specific needs, making them more powerful and efficient.

The AI landscape is evolving faster than ever, and with models like DeepSeek and Qwen 2.5 Max, we’re not just keeping up—we’re leading the charge.

Lessons from the Bay: Founders and Fast Pivots

This month, our CEO and founder, Ayush Shrivastava, has been repping Quash in San Francisco, USA—meeting with potential partners, exploring sales opportunities, and soaking in the tech culture of the Bay Area. And let’s just say, it’s been an insightful experience.

One thing that stood out is the fearless founder mindset there. Failing fast, iterating rapidly, and scaling aggressively are the norm. Pivoting isn’t just accepted—it’s celebrated. Compared to Bangalore, where pivoting can feel like a setback, San Francisco’s pace and boldness is truly inspiring.

The networking effects in San Francisco play a huge role in shaping this culture. The proximity to tech giants, top-tier universities, and a thriving startup ecosystem creates a unique environment. From founder dinners to tech events hosted by major organizations and universities, the opportunities to connect and learn are endless. Universities like Stanford and Berkeley are deeply invested in fostering innovation—Stanford’s AI lab and Berkeley’s startup incubation programs are just two examples. Plus, it’s not uncommon to see professors doubling as angel investors, actively supporting early-stage ventures. Seeing this ecosystem firsthand was a reminder of how powerful collaboration can be—and hopefully, similar support structures can develop in India to fuel more innovation.

On that note, we're excited to announce an exclusive event in Bangalore focused on AI Agents, AI Builders, and Retrieval-Augmented Generation (RAG). The event will bring together top AI experts and CTOs from innovative companies to discuss real-world applications of these technologies. It’s a fantastic opportunity to dive deep into how AI is transforming industries and to network with professionals who share a similar passion for innovation. Stay tuned for more details on the date and speakers!

Bay Area Pivots + AI Breakthroughs: DeepSeek vs Qwen 2.5 Max Unveiled