Top-Rated LLMs for Chat in 2025

The rapid rise of large language models (LLMs) has transformed how we build modern AI applications. They now power everything from customer support chatbots to complex LLM agents that can reason, plan, and take actions across tools.

For many AI teams, closed-source options like GPT-5 and Claude Sonnet 4 are convenient. With just a simple API call, you can prototype an AI product in minutes — no GPUs to manage and no infrastructure to maintain. However, this convenience comes with trade-offs: vendor lock-in, limited customization, unpredictable pricing and performance, and ongoing concerns about data privacy.

That’s why open-source LLMs have become so important. They let developers self-host models privately, fine-tune them with domain-specific data, and optimize inference performance for their unique workloads.

In this post, we’ll explore the top-rated open-source LLMs for chat in 2025. After that, we’ll answer some of the FAQs teams have when evaluating LLMs for production use.

DeepSeek-V3.1#

DeepSeek-V3.1 is one of the best open-source LLMs available today. Built with a Mixture-of-Experts (MoE) architecture, it combines the strengths of predecessors V3 and R1 into a single hybrid model. It features a total of 671B parameters (37B activated) and supports context lengths up to 128K.

The model is built on DeepSeek-V3.1-Base, trained with an expanded long-context process: 630B tokens for the 32K phase and 209B tokens for the 128K phase.

DeepSeek came to the spotlight during the “DeepSeek moment” in early 2025, when its R1 model demonstrated ChatGPT-level reasoning at significantly lower training costs. Now that DeepSeek-V3.1 integrates reasoning capabilities, it's unclear whether they still plan to release a standalone DeepSeek-R2 model. Learn about the latest DeepSeek model series.

Why should you use DeepSeek-V3.1:

• Hybrid thinking mode: DeepSeek-V3.1 supports both thinking and non-thinking modes. You can switch between fast responses and deeper reasoning simply by changing the chat template. This makes it highly adaptable for different types of chat and agentic tasks.
• Efficient reasoning performance: The V3.1-Think variant achieves reasoning quality on par with DeepSeek-R1-0528 but responds faster. DeepSeek’s internal evaluations show that with chain-of-thought compression training, it reduces output tokens by 20–50% while maintaining similar performance.
• Advanced tool use: Post-training optimization has improved tool usage. V3.1 outperforms both DeepSeek-V3-0324 and DeepSeek-R1-0528 in benchmarks for code agents and search agents. This makes it an ideal choice for building LLM agents.
• Fully open-source: Released under the permissive MIT License, DeepSeek-V3.1 is free to use for commercial, academic, and personal projects. It's an attractive option for teams building self-hosted LLM deployments, especially those looking to avoid vendor lock-in.

Also note that DeepSeek-V3.1 requires substantial compute resources. Running it efficiently requires multi-GPU setups, like 8 NVIDIA H200 (141GB of memory) GPUs. This level of infrastructure is cost-prohibitive for smaller teams or individual developers.

gpt-oss-120b#

gpt‑oss‑120b is OpenAI’s most capable open-source LLM to date. With 117B total parameters and a Mixture-of-Experts (MoE) architecture, it rivals proprietary models like o4‑mini. More importantly, it’s fully open-weight and available for commercial use.

OpenAI trained the model with a mix of reinforcement learning and lessons learned from its frontier models, including o3. The focus was on making it strong at reasoning, efficient to run, and practical for real-world use. The training data was mostly English text, with a heavy emphasis on STEM, coding, and general knowledge. For tokenization, OpenAI used an expanded version of the tokenizer that also powers o4-mini and GPT-4o.

The release of gpt‑oss marks OpenAI’s first fully open-weight LLMs since GPT‑2. It has already seen adoption from early partners like Snowflake, Orange, and AI Sweden for fine-tuning and secure on-premises deployment.

Why should you use gpt‑oss‑120b:

• Excellent performance: gpt‑oss‑120b matches or surpasses o4-mini on core benchmarks like AIME, MMLU, TauBench, and HealthBench (even outperforms proprietary models like OpenAI o1 and GPT‑4o).

• Efficient and flexible deployment: Despite its size, gpt‑oss‑120b can run on a single 80GB GPU (e.g., NVIDIA H100 or AMD MI300X). It's optimized for local, on-device, or cloud inference via partners like vLLM, llama.cpp and Ollama.

• Adjustable reasoning levels: It supports low, medium, and high reasoning modes to balance speed and depth.

  • Low: Quick responses for general use.
  • Medium: Balanced performance and latency
  • High: Deep and detailed analysis.

• Permissive license: gpt‑oss‑120b is released under the Apache 2.0 license, which means you can freely use it for commercial applications. This makes it a good choice for teams building custom LLM inference pipelines.

Qwen3-235B-A22B-Instruct-2507#

Alibaba has been one of the most active contributors to the open-source LLM ecosystem with its Qwen series. Qwen3 is the latest generation, offering both dense and MoE models across a wide range of sizes. At the top of the lineup is Qwen3-235B-A22B-Instruct-2507, an updated version of the earlier Qwen3-235B-A22B’s non-thinking mode.

This model has 235B parameters, with 22B active per token, powered by 128 experts (8 active). Note that it only supports non-thinking mode and does not generate <think></think> blocks. You can try Qwen3-235B-A22B-Thinking-2507 for more complex reasoning tasks.

Why should you use Qwen3-235B-A22B-Instruct-2507:

• State-of-the-art performance: The model sees significant gains in instruction following, reasoning, comprehension, math, science, coding, and tool use. It outperforms models like GPT-4o and DeepSeek-V3 on benchmarks including GPQA, AIME25, and LiveCodeBench.
• Ultra-long context: It features a context length of 262,144 natively and extendable up to over 1 million tokens. This makes it a perfect choice for systems like AI agents, RAG, and long-term conversations. Keep in mind that to process sequences at this scale, you need around 1000 GB of GPU memory (model weights, KV-cache storage, and peak activation memory demands).
• Multilingual strength: Instruct-2507 supports 100+ languages and dialects, with better coverage of long-tail knowledge and stronger multilingual instruction-following than previous Qwen models.

The Qwen team does not stop with Instruct-2507. They note a clear trend: scaling both parameter count and context length for building more powerful and agentic AI. Their answer is the Qwen3-Next series, which focuses on improved scaling efficiency and architectural innovations.

The first release, Qwen3-Next-80B-A3B, comes in both Instruct and Thinking versions. The instruct variant performs on par with Qwen3-235B-A22B-Instruct-2507 on several benchmarks, while showing clear advantages in ultra-long-context tasks up to 256K tokens.

Since Qwen3-Next is still very new, there’s much more to explore. We’ll be sharing more updates later.

 

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Now let’s take a quick look at some of the FAQs around LLMs.

Why should I choose open-source LLMs over proprietary LLMs?#

The decision between open-source and proprietary LLMs depends on your goals, budget, and deployment needs. Open-source LLMs often stand out in the following areas:

• Customization. You can fine-tune open-source LLMs for your own data and workloads. Additionally, you can apply inference optimization techniques such as speculative decoding, prefix caching and prefill-decode disaggregation for your performance targets. Such custom optimizations are not possible with proprietary models.
• Data security. Open-source LLMs can be run locally, or within a private cloud infrastructure, giving users more control over data security. By contrast, proprietary LLMs require you to send data to the provider’s servers, which can raise privacy concerns.
• Cost-effectiveness. While open-source LLMs may require investment in infrastructure, they eliminate recurring API costs. With proper LLM inference optimization, you can often achieve a better price-performance ratio than relying on commercial APIs.
• Community and collaboration. Open-source projects benefit from broad community support. This includes continuous improvements, bug fixes, new features, and shared best practices driven by global contributors.
• No vendor lock-in. Using open-source LLMs means you don’t rely on a single provider’s roadmap, pricing, or availability.

How can I optimize LLM inference performance?#

One of the biggest benefits of self-hosting open-source LLMs is the flexibility to apply inference optimization for your specific use case. Frameworks like vLLM and SGLang already provide built-in support for inference techniques such as continuous batching and speculative decoding.

But as models get larger and more complex, single-node optimizations are no longer enough. The KV cache grows quickly, GPU memory becomes a bottleneck, and longer-context tasks such as agentic workflows stretch the limits of a single GPU.

That’s why LLM inference is shifting toward distributed architectures. Optimizations like prefix caching, KV cache offloading, data/tensor parallelism, and prefill–decode disaggregation are increasingly necessary. While some frameworks support these features, they often require careful tuning to fit into your existing infrastructure. As new models are released, these optimizations may need to be revisited.

At Bento, we help teams build and scale AI applications with these optimizations in mind. You can bring your preferred inference backend and easily apply the optimization techniques for best price-performance ratios. Leave the infrastructure tuning to us, so you can stay focused on building applications.

What should I consider when deploying LLMs in production?#

Deploying LLMs in production can be a nuanced process. Here are some strategies to consider:

1. Model size: Balance accuracy with speed and cost. Smaller models typically deliver faster responses and lower GPU costs, while larger models can provide more nuanced reasoning and higher-quality outputs. Always benchmark against your workload before committing.
2. GPUs: LLM workloads depend heavily on GPU memory and bandwidth. For enterprises to self-host LLMs (especially in data centers), NVIDIA A100, H200, B200 or AMD MI300X, MI350X, MI355X are common choices. Similarly, benchmark your model on the hardware you plan to use. Tools like llm-optimizer can quickly help find the best configuration.
3. Scalability: Your deployment strategy should support autoscaling up or down based on demand. More importantly, it must happen with fast cold starts or your user experience suffers.
4. LLM-specific observability: Apart from traditional monitoring, logging and tracing, also track inference metrics such as Time to First Token (TTFT), Inter-Token Latency (ITL), and token throughput.

Final thoughts#

The rapid growth of open-source LLMs has given teams more control than ever over how they build AI applications. They are closing the gap with proprietary ones while offering unmatched flexibility.

At Bento, we help AI teams unlock the full potential of self-hosted LLMs. By combining the best open-source models with tailored inference optimization, you can focus less on infrastructure complexity and more on building AI products that deliver real value.

To learn more about self-hosting LLMs:

• Read our LLM Inference Handbook
• Benchmark and optimize LLM performance with llm-optimizer
• Join the BentoML Slack community to connect with other builders
• Sign up for our inference platform and deploy your first LLM in the cloud
• Schedule a call with us to discuss your LLM use case