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When we talk about LLMs, most people immediately think about frontier models.
Claude, GPT, Gemini and other top-tier models are extremely capable. But that capability comes with a cost. For many production systems, using the strongest model for every task is not engineering. It is waste.
At the same time, we are seeing a new wave of specialized models performing very well in specific areas: coding, tool calling, long context, image generation, audio, extraction, routing and structured reasoning.
Models like Qwen, Kimi, MiniMax and others are becoming more relevant because they solve real problems with better cost efficiency.
The future of AI systems is not about always choosing the most powerful model.
It is about choosing the right model for the right task.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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My Submission for #elevencreative #contra challenge. Check out my template here - https://elevenlabs.io/app/templates/zfrP1cyhZ6rL1Q43MHGJ
Very strong execution here!
I built the thing I wished existed — an agent-driven ad pipeline — on @melius, and used it to ship a full brand campaign for Stratos Markets, my prediction-markets startup, in an afternoon.
I program agentic systems for a living but had yet to attempt fully autonomous agentic media workflows. So I treated it as a build problem: a six-beat brand runbook as the spec, one brand anchor as source of truth, Melius wired to Claude Desktop over MCP, Claude Opus 4.7 executing the canvas node by node. I never placed a pixel, just reviewed outputs against spec.
The canvas mapped cleanly to how I already build: typed nodes, deterministic outputs, a graph I can reason about. That fit is what let an engineer ship a brand-coherent campaign in an afternoon.
30s walkthrough below.
Interesting app! Good work 🙌
Most AI design tools get tested on what they can generate. I tested Claude Design on whether it could work inside a system that already existed.
What I loved about it is that it reasons before it builds. It had a point of view. It flagged its own mistakes before I caught them.
What slipped a bit for me: give it nuanced information and it'll smooth it over to sound cohesive. It can lose accuracy chasing consistency.
It's more of a thinking collaborator than a generator - but the judgment calls are still yours.
Where do you draw the line with AI in your creative process? Drop it below.
(Full Breakdown👇🏻)
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