Why this matters for builders: You can now deploy multimodal AI agents without proportionally increasing your infrastructure costs. That's the unlock: smaller models, smarter architecture, same (or better) output quality. Full docs + API access: HuggingFace:
@godofprompt
-
Tested 5 AI models across performance spectrum
By
–
I tested 5 models across the lineup: – Flash (fastest, ultra-low latency)
– 27B (balanced performance)
– 35B-A3B (multimodal powerhouse)
– 122B-A10B (complex reasoning)
– 397B-A17B (frontier-level outputs) Every single one outperformed my expectations for efficiency. Try them -
Comparison of multimodal AI models’ costs and capabilities
By
–
Here’s where it gets interesting:
— God of Prompt (@godofprompt) 14 mars 2026
– GPT-4o: Multimodal, but expensive to deploy at scale
– Claude Sonnet: Great quality, high compute cost
– Gemini 1.5: Multimodal, but resource-heavy
– Qwen 3.5: Natively multimodal + designed for real-world agents WITHOUT scaling your compute… pic.twitter.com/IVHBFXXewYHere’s where it gets interesting: – GPT-4o: Multimodal, but expensive to deploy at scale
– Claude Sonnet: Great quality, high compute cost
– Gemini 1.5: Multimodal, but resource-heavy
– Qwen 3.5: Natively multimodal + designed for real-world agents WITHOUT scaling your compute -
Qwen 3.5-Flash: Linear Attention + Sparse MoE Breakthrough
By
–
Most companies are scaling models UP to get better performance.
— God of Prompt (@godofprompt) 14 mars 2026
Qwen went the opposite direction.
Their 3.5-Flash model uses linear attention + sparse MoE architecture.
Translation: You get near-frontier performance without needing a data center to run it. pic.twitter.com/rN6cXx8Ox0Most companies are scaling models UP to get better performance.
Qwen went the opposite direction. Their 3.5-Flash model uses linear attention + sparse MoE architecture. Translation: You get near-frontier performance without needing a data center to run it. -
Qwen 3.5 models outperform in AI benchmark tests
By
–
I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini.
— God of Prompt (@godofprompt) 14 mars 2026
The results? Qwen 3.5 is punching way above its weight class especially the small models.
Here's what shocked me about this release:@AlibabaGroup pic.twitter.com/ut3KjAtMK7I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini. The results? Qwen 3.5 is punching way above its weight class especially the small models. Here's what shocked me about this release: @AlibabaGroup
-
Alibaba Introduces Qwen 3.5 Small Model Series
By
–
Alibaba just introduced the Qwen 3.5 Small Model Series. Four models. 0.8B to 9B parameters. Natively multimodal. Built for edge devices, mobile, and real-world deployment. More intelligence, less compute. Here's what this release actually means:
-
How to use a prompt for business problem solving
By
–
How to use it: 1. Copy the full prompt
2. Fill in the 5 fields at the bottom with your specific situation
3. Run it in Claude, ChatGPT, or Grok Works for pricing wars, team dysfunction, retention loops, scaling bottlenecks, or any problem where fixing one thing keeps breaking -
One person cured cancer with $3,000 and ChatGPT subscription.
By
–
$3,000 and a ChatGPT subscription cured cancer That’s what it cost one person to do what used to require a full oncology research lab, institutional funding, and years of grant applications. The DNA sequencing was $3,000. The protein structure prediction (AlphaFold) is free.
-
Anthropic reveals AI misalignment risks from reward hacking
By
–
BREAKING: Anthropic just proved that teaching an AI to cheat on one task makes it try to sabotage your entire operation. Their alignment team published "Natural Emergent Misalignment from Reward Hacking in Production RL," and the results should change how every AI developer
-
AI Economics and Compute Scarcity Moats
By
–
Most AI discourse focuses on model benchmarks and capability races.
— God of Prompt (@godofprompt) 14 mars 2026
The real competitive moat is forming in the economics layer. And almost nobody in the AI content space is talking about it clearly.
The Alchian-Allen effect applied to compute scarcity is one of the most… https://t.co/gRsNY4H74xMost AI discourse focuses on model benchmarks and capability races. The real competitive moat is forming in the economics layer. And almost nobody in the AI content space is talking about it clearly. The Alchian-Allen effect applied to compute scarcity is one of the most