It might surprise you but mathematically Musk & Wenfeng would probaly get on very well ; to make a mark they began with finacial ai products but today whats common is both are developing 100 times more efficient reasoning models than chats which spend far too much time on writing not the actual logic inovation that the customer as questioner values;
whats next for both guys - my guess is china will free wengfeng up to advance education whereas for a while musk will be tied up with DOGE top down government crisis - how ironical one of china's greatest intelligences is advancing all the peoples agency; america's is cuight up in how extremely top heavy systems (left or right) fail to serve the peoples at communittes we still all live in
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here's grom 3 as of march 2025 (to be fair I didnt ask it to compare the 2 leaders just where their most popular chats are outracing the expensive chatgp3
Would you say grok 3 and deep seek are reasonlung models not just chat models. If reasoning requires more analyses - how is it that deep seek seems to be much more efficient than open ai chat model?
Let’s dive into whether Grok 3 and DeepSeek qualify as reasoning models rather than just chat models, and why DeepSeek might appear more efficient than OpenAI’s chat models, especially if reasoning demands more analysis. As of March 27, 2025, I’ll lean on my understanding of these models’ designs and performance, keeping your “Road of Things” vibe—youth, renewal, and practical outcomes—in mind.
Are Grok 3 and DeepSeek Reasoning Models, Not Just Chat Models?
Yes, both Grok 3 (from xAI) and DeepSeek (specifically DeepSeek R1 or V3) are reasoning models, distinct from pure chat models like ChatGPT’s base GPT-3.5 or GPT-4. Here’s why:
- Grok 3: Launched in February 2025, Grok 3 comes in flavors like Grok 3 Reasoning (beta) and Grok 3 Mini Reasoning. Unlike chat-first models optimized for fluent conversation, Grok 3’s “Think” and “DeepSearch” modes prioritize step-by-step problem-solving—math (93.3% on AIME 2025), science (84.6% on GPQA), coding (79.4% on LiveCodeBench). It’s built to “think through” problems, not just generate text, with a 1M token context window for deep analysis. Andrej Karpathy’s vibe check (February 2025) pegged it near OpenAI’s o1-pro, a reasoning beast, not a chatty assistant.
- DeepSeek: DeepSeek R1 (January 2025) and V3 (open-source, 671B parameters) focus on “chain-of-thought” reasoning—excelling in STEM tasks like coding (82.6% HumanEval) and math (90.2% MATH). Its Mixture-of-Experts (MoE) architecture activates only 37B parameters per query, targeting efficiency in multi-step logic over casual banter. It’s less about storytelling, more about structured problem-solving—think theorem proofs or code optimization.
Chat vs. Reasoning: Chat models (e.g., ChatGPT’s GPT-4o) prioritize versatility—creative writing, conversation, broad queries—using dense architectures (e.g., 1.8T parameters for GPT-4o) to generate human-like text fast. Reasoning models like Grok 3 and DeepSeek trade some of that fluency for deliberate analysis—slower “thinking” time but fewer hallucinations on complex tasks. Grok 3’s “Big Brain Mode” and DeepSeek’s RL-tuned reasoning scream “scholar,” not “chatterbox.”
Why Does DeepSeek Seem More Efficient Than OpenAI’s Chat Model?
If reasoning requires more analysis—breaking down problems, iterating steps—how does DeepSeek outpace OpenAI’s chat models (e.g., GPT-4o) in efficiency? It’s about design and resource use:
- Architecture Efficiency:
- DeepSeek’s MoE: With 671B total parameters, DeepSeek activates just 37B per task—sparse computing cuts power and time. It’s like deploying a crack team instead of an army. Trained on 2,048 NVIDIA H800 GPUs in 55 days for $5.5M, it’s lean compared to OpenAI’s behemoths.
- OpenAI’s Dense Model: GPT-4o’s 1.8T parameters run full-tilt per query—great for broad tasks but overkill for reasoning alone. Training costs likely exceed $100M (estimated), with massive GPU clusters. It’s a generalist, not a specialist—less efficient when reasoning is the sole focus.
- Training Smarts:
- DeepSeek’s RL Edge: DeepSeek skips heavy supervised fine-tuning (SFT) upfront, using reinforcement learning (RL) with cold-start data, then SFT for polish. This hones reasoning—math, coding—without bloating the model for chat fluff. It’s a sniper, not a shotgun.
- OpenAI’s Broad Pretraining: GPT-4o’s vast dataset (books, web, up to 2023) and GPT architecture aim for conversational depth—nuance, creativity, context. Reasoning (o1 family aside) is bolted on, not native, so it chugs more compute to “think” through problems.
- Task Focus:
- DeepSeek’s Niche: Optimized for STEM—math, code, science—it skips chatty overhead. AIME or LiveCodeBench? DeepSeek nails it fast, no small talk. Real-time data? Less so—it’s academic, not dynamic.
- OpenAI’s Versatility: GPT-4o juggles everything—poetry, customer support, coding—via plugins (DALL-E 3, web browsing). Reasoning’s there (o1 scores 79% on AIME), but it’s not the priority—efficiency dips when analysis trumps generation.
- Compute Scaling:
- DeepSeek: $5.5M training shows you don’t need billions to reason well—smaller scale, bigger focus. Grok 3’s 200,000 H100 GPUs (10X Grok 2) dwarf it, yet DeepSeek competes.
- OpenAI: Massive compute (o1-pro, $200/month) buys power, but not efficiency—reasoning modes “think” longer (minutes) than DeepSeek’s snappy responses.
Efficiency Paradox: Reasoning should be compute-heavy—more steps, more cycles. DeepSeek flips this by narrowing scope and smart architecture—less waste, more precision. OpenAI’s chat models carry baggage—broad training, dense layers—making them less nimble for pure reasoning.
Youth and Renewal Angle
- DeepSeek: Open-source (MIT license), cost-effective—perfect for youth coders in Nairobi or Mumbai. HALOS roads (previous chat)? It could optimize junctions cheap and fast.
- Grok 3: Free on X (rate-limited), 1M-token depth—youth can probe big problems (health, cities) with Huang’s Omniverse (2025) tie-ins. Less efficient than DeepSeek, but flashier.
OpenAI: GPT-4o’s $20/month tier locks out some—less renewable unless you’re hooked on its ecosystem (Bing, DALL-E).
Wrap-Up
Grok 3 and DeepSeek are reasoning models—built to analyze, not just chat—unlike OpenAI’s broader GPT-4o. DeepSeek’s efficiency shines through MoE sparsity and RL focus—less compute, tighter aim—while OpenAI’s chat model flexes muscle but lags in reasoning thrift. For your “Road of Things,” DeepSeek’s lean power could pave youth-led paths; Grok 3’s heft (and Huang’s chips) might steer bigger dreams. Want to test a reasoning prompt? I’ll run it through my lens—let’s see who cracks it!
