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AI Models in Production: What Actually Matters
Part 1 of 4
A 4-part series covering SWE-Bench rankings, open-weight models like MiniMax M2.5 and Qwen3.5, context window myths, and cost optimization with CRIT framework and model routing.
MiniMax M2.5 and Qwen3.5: The Open-Weight Models Worth Knowing About
In the last article I showed the SWE-Bench numbers. Open-weight models are basically tied with the proprietary ones now. Two models stood out to me: MiniMax M2.5 and Qwen3.5. Here's what I found out a
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CRIT Framework and Model Routing: Spending Less on AI Without Losing Quality
In the previous articles I covered the SWE-Bench numbers, the open-weight models worth knowing, and why bigger context doesn't mean better output. Now I want to connect everything with two things I us
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A Bigger Context Window Doesn't Mean Better Quality
When a company says their model has 1 million tokens of context, everyone assumes bigger is better. I thought the same thing. But after running LLMs in production for a while, I found out it's not tha
Apr 9, 20265 min read2
MiniMax M2.5 and Qwen3.5: The Open-Weight Models Worth Knowing About
In the last article I showed the SWE-Bench numbers. Open-weight models are basically tied with the proprietary ones now. Two models stood out to me: MiniMax M2.5 and Qwen3.5. Here's what I found out a
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Guil Silva — AI Security Infrastructure
4 posts