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William Suffill

wsuff

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wsuff

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reacted to eugenesiow's post with 👀 about 12 hours ago

GPT-4.1 dropped this week - and it puts OpenAI back in the race for coding & agentic leadership. ⚙️ API only - no ChatGPT toggle for this. 💻 Coding performance is back on par with Claude 3.7 Sonnet & Gemini 2.5 Pro (though Gemini still leads). 💸 Pricing: • Full: $3.50 / 1M tokens • Mini: $0.70 / 1M • Nano: $0.17 / 1M 👉 Gemini 2.5 Pro = best price/perf ($3.44 / 1M) 😵 Claude 3.5 Sonnet = $6 / 1M (!) 🧠 Not a "thinking" model. 📊 Mini shines on general reasoning tasks (e.g. GPQA), but only the full model holds up in SWE-bench-verified (GitHub issue solving).

reacted to m-ric's post with 🚀 1 day ago

New king of open VLMs: InternVL3 takes Qwen 2.5's crown! 👑 InternVL have been a wildly successful series of model : and the latest iteration has just taken back their crown thanks to their superior, natively multimodal vision training pipeline. ➡️ Most of the vision language models (VLMs) these days are built like Frankenstein : take a good text-only Large Language Model (LLM) backbone, stitch a specific vision transformer (ViT) on top of it. Then the training is sequential 🔢 : 1. Freeze the LLM weights while you train the ViT only to work with the LLM part, then 2. Unfreeze all weights to train all weights in order to work together. 💫 The Shanghai Lab decided to challenge this paradigm and chose this approach that they call "native". For each of their model sizes, they still start from a good LLM (mostly Qwen-2.5 series, did I tell you I'm a huge fan of Qwen? ❤️), and stitch the ViT, but they don't freeze anything : they train all weights together with interleaved text and image understanding data in a single pre-training phase 🎨. They claim it results in more seamless interactions between modalities. And the results prove them right: they took the crown of top VLMs, at nearly all sizes, from their Qwen-2.5 parents. 👑

liked a model 5 days ago

agentica-org/DeepCoder-14B-Preview

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