Comparing Open-Source LLMs and Advanced Reasoning Models

Choosing between open-source and proprietary LLMs isn't just about cost but also about control, flexibility, and how you want to build your AI future. Open-source models like LLaMA and Mistral give you complete ownership and customization freedom, while proprietary options like GPT-4 and Claude deliver cutting-edge performance with enterprise support. Your decision determines everything from your development workflow to your long-term strategic independence. Here's how these two approaches differ across the factors that matter most: 🔹 Control & Customization: - Open-source models let you fine-tune everything: weights, architecture, training data, and deployment environment. You own the entire stack and can modify it however you need. - Proprietary models lock you into their API ecosystem with limited customization options, though some offer adapters or fine-tuning services. 🔹 Performance & Innovation: - Proprietary models currently lead in raw capability and benchmark performance, backed by massive research teams and computational resources. - Open-source models are catching up rapidly thanks to global community contributions, but often trail behind the latest proprietary breakthroughs by several months. 🔹 Deployment & Infrastructure: - Open-source gives you complete deployment flexibility - run locally, on your cloud, or at the edge with full control over latency and uptime. - Proprietary models force you to use their infrastructure, creating dependency on their servers, pricing, and service availability. 🔹 Cost & Vendor Lock-in: - Open-source models cost less long-term since you only pay for compute, not per-token fees that scale with usage. However, you manage the infrastructure complexity yourself. - Proprietary models charge per API call, which can get expensive at scale, and they tie you to their pricing structure and platform limitations. Open-source builds long-term strategic independence while proprietary delivers immediate cutting-edge results. Your choice depends on whether you prioritize control and cost-effectiveness or want the latest performance with minimal setup effort. #llm #artificialintelligence

No hype -- just facts. 😊 Spent the morning pouring over the GPT-OSS technical report and here's what I've got. OpenAI just released two open source (Apache 2.0) Mixture of Experts (MoE) reasoning models trained for tool use: gpt-oss-20b and gpt-oss-120b. What makes these models special? • They're fully open-weight models with performance similar to paid models like o3-mini and 04-mini • The 20B can run on edge devices and consumer hardware • Both support a massive 130k+ token context length • MoE architecture that makes them efficient despite their size • Strong partnerships with deployment platforms and optimized for compute hardware These models are designed for use in agentic workflows with strong reasoning, tool use, and instruction-following capabilities. You can adjust the reasoning level (low, medium, high) to balance speed vs. depth of analysis. The tool use capabilities are particularly impressive - the models can: • Browse the web to fetch current information • Execute Python code in a Jupyter notebook environment • Call custom functions that you define Performance-wise, gpt-oss-120b actually exceeds OpenAI o3-mini on standard benchmarks like MMLU, GPQA, and coding tasks. Even the smaller 20B model performs surprisingly well despite being 6x smaller than its larger sibling. The models use a special "harmony chat format" that enables advanced features like interleaving tool calls within reasoning steps. The gpt-oss models work out-of-the-box with hardware and deployment providers, thanks to several key partnerships. Fine-tunable with: Hugging Face, Unsloth AI, LLaMA-Factory, Ludwig Deployable with: Hugging Face, Ollama, vLLM, Llama.cpp, OpenRouter, LM Studio, Fireworks AI, Baseten, Vercel, Databricks, Azure, Amazon Web Services (AWS) Optimized for: NVIDIA, AMD, Groq, Cerebras Systems For more details, especially on the training process, adversarial testing, and model performance, check out the blog post or model card. 🔗 Blog: https://lnkd.in/geapnGDE 📄 Model card: https://lnkd.in/gFnYuTUT

The trajectory of research for open LLMs and open reasoning models has been shockingly similar, but there are still many open questions… Phase One: Everything begins with the release of a powerful, open model. For general LLM research, this model was LLaMA, which enabled tons of downstream research (e.g., Alpaca, Vicuna, Koala, etc.). For reasoning models, this model was DeepSeek-R1. Both of these models were used as a starting point for research and the creation of hundreds of model variants. Phase Two: Once a powerful open model is made available, the research community can explore a vast number of research topics in parallel. Very quickly, we saw LLM researchers use LLaMA to create open replications of closed models; e.g., by taking LLaMA as a base model and training this model over completions from a closed model (see the Orca paper). Similarly, we are starting to see researchers explore this strategy for reasoning models. Sky-T1 is an open replication of o1-style reasoning models with an open training dataset. Bespoke Stratos is a similar model that invests more into data curation to improve model quality. These models perform very well and are extremely cheap to train <$1000. Phase Three: Once we can replicate closed models openly, we can begin to optimize for costs. For example, we saw with LIMA that we can train models similar to ChatGPT using only 1,000 high-quality training examples for SFT. Here, the main finding is that it’s very easy to adapt a good base / starting model to accomplish a variety of useful tasks. Similarly, LIMO shows us that we can train powerful reasoning models with only 817 training examples! Going further, DeepSeek-R1 trains numerous dense / distilled / small versions of the R1 model that are more cost effective and achieve similar reasoning performance. If we have good reasoning data, training a decent reasoning model just requires some SFT. Phase Four: For mainstream LLM research, these open replications of closed models eventually led to a paper that showed distilled / smaller models do NOT fully replicate powerful closed models. Smaller models have very good style / fluency, which can hide gaps in their knowledge and general capabilities. So, we lose something by replacing a big, powerful model with a more lightweight / inexpensive open replication. A similar paper has not yet been published for reasoning models. It is very possible that the same conclusion will hold – smaller / distilled models may have gaps in performance compared to full reasoning models that have yet to be discovered. However, reasoning models may also behave differently; e.g., they may generalize better due to the structure of their training data or from using RL during finetuning (e.g., see Sky-T1-Flash). To me, these are pivotal questions to answer for current research on open reasoning models: - Do the smaller / distilled models generalize well? - Are we missing any gaps in performance?

The recently released open-source model, DeepSeek-R1, is comparable to OpenAI O1 in multiple benchmarks. This O1-level complex reasoning ability, as mentioned in the paper, is due to "emergent long Chain-of-Thought (CoT) from large-scale reinforcement learning." This might sound alienating, but it should feel rather familiar after a brief reflection. Let's understand by contrasting the training recipes of standard LLMs vs reasoning "thinking" LLMs. Simplified recipe of training SOTA LLMs (DeepSeek-V3, Llama, Qwen, ...): 📌 Pre-training: Starting from random weights, train on ~10-20 Trillion token data 📌 Post-training: ✔️ Supervised fine-tune (SFT) with millions of (prompt, response) pairs ✔️ Perform preference learning (RLHF/PPO/DPO) with millions of human preferences or labels from reward models Before going over the recipe for thinking reasoning models, let's consider why we are building these new types of models. For some hard problems, like Olympiad-level math problems, rather than giving one answer, giving multiple answers or trying different approaches proved to be helpful. Even better if there is an internal thought process (long CoT) that tries an approach, regularly introspects itself and changes the approach, or explores new strategies if the previous one is not satisfactory. See the contrast here: standard CoT breaks down the problem into multiple steps but tries only one approach. This came to be known as inference-time scaling (at least, one way of doing this scaling). As in, we generate more tokens based on the complexity of the problem (could be tens of thousands of tokens) while inferring from a model. These extra generated tokens for exploration could be treated as ‘internal thinking’ tokens. (See an LLM having an 'aha moment' in one of the images attached). Usually, these thinking models are trained in math, coding, or logic-heavy domains. These tasks/questions have a correct answer and the correctness acts as a reward/preference for RL. Simplified recipe of training reasoning LLMs: (DeepSeek-R1, possibly OpenAI O1/O3) 1️⃣ After pre-training, perform SFT on very few warm-up data rather than millions of (prompt, response) pairs. 2️⃣ Perform RL for many iterations with correctness as the reward model. This is where long CoT emerged just from trial and error of RL training. 3️⃣ Perform SFT on large curated data for broad tasks. 4️⃣ Perform RL again with correctness as the reward model for math/code tasks. And LLM-as-a-judge type generative reward model for other tasks. It is important to note that emergent long CoT, internal thinking, with large-scale RL was only possible in domains where we know the exact answers. It is still a research exploration acquiring complex reasoning abilities outside of these domains.

𝐓𝐫𝐚𝐜𝐞𝐚𝐛𝐥𝐞 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐋𝐋𝐌𝐬: 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤-𝐑𝟏 𝐨𝐯𝐞𝐫 𝐎𝐩𝐞𝐧𝐀𝐈 𝐨𝟑-𝐦𝐢𝐧𝐢 One week apart, 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤 𝐑𝟏 and 𝐨𝟑-𝐦𝐢𝐧𝐢 have been released. I want to share my perspective on these LLMs with reasoning (a.k.a., chain-of-thought), drawing on my experiences as a vertical AI builder. When #OpenAI’s o1 model first came out, I was skeptical about the future of reasoning LLMs because true productization seemed impossible. Our team, LinqAlpha, develops vertical AI agentic solutions, currently being used by around 100 hedge funds and asset managers, so we have to care about core aspects of any LLM product: (1) output quality, (2) output speed/cost, and (3) reasoning consistency. o1 (and o3-mini as well) hinted at promising reasoning quality, suggesting that high-level reasoning was within reach. However, o1 often ran 10 times slower than what was feasible for production and was also prohibitively expensive, making real-world scalability difficult. Similarly, even though o3-mini offers improvements in speed, 𝐛𝐨𝐭𝐡 𝐨𝟏 𝐚𝐧𝐝 𝐨𝟑-𝐦𝐢𝐧𝐢 𝐬𝐡𝐚𝐫𝐞 𝐚 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐥𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐰𝐡𝐞𝐧 𝐮𝐬𝐞𝐝 𝐯𝐢𝐚 𝐀𝐏𝐈: 𝐰𝐞 𝐨𝐧𝐥𝐲 𝐬𝐞𝐞 𝐭𝐡𝐞 𝐟𝐢𝐧𝐚𝐥 𝐨𝐮𝐭𝐩𝐮𝐭𝐬 𝐰𝐢𝐭𝐡 𝐧𝐨 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐯𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲. This lack of transparency not only makes it impossible to debug or refine the reasoning process, but it also means that reasoning consistency cannot be properly verified. Without visibility into the chain-of-thought, achieving true productization is out of reach. 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤-𝐑𝟏 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐭𝐡𝐞 𝐠𝐚𝐦𝐞 𝐛𝐲 𝐞𝐱𝐩𝐨𝐬𝐢𝐧𝐠 𝐢𝐭𝐬 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐬𝐭𝐞𝐩𝐬, 𝐰𝐡𝐢𝐜𝐡 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐮𝐧𝐩𝐫𝐞𝐜𝐞𝐝𝐞𝐧𝐭𝐞𝐝 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬. This transparency reduces the need for hard-coded logic in complex, domain-specific workflows involving hundreds or even thousands of agentic nodes. We’re seeing early signs of a potential 10–20% reduction in these agentic nodes, thanks to R1’s traceable reasoning. Ultimately, as reasoning LLMs continue to mature, I expect we’ll see a shift toward fewer rule-based components and greater flexibility in domain-specific workflow design. For those looking to develop robust reasoning LLM-based products, we will be sharing our reasoning-focused code that integrates with 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤-𝐑𝟏. We want to help build solutions that were previously out of reach with opaque models. 𝐎𝐩𝐞𝐧𝐀𝐈, 𝐩𝐥𝐞𝐚𝐬𝐞 𝐨𝐩𝐞𝐧 𝐮𝐩 𝐲𝐨𝐮𝐫 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬, 𝐬𝐨 𝐢𝐭 𝐜𝐚𝐧 𝐭𝐫𝐮𝐥𝐲 𝐛𝐞 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐟𝐨𝐫 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬.