Best Open-Weight LLMs for Agentic Chat (December 2025)
Overview and Criteria

In the rapidly evolving LLM landscape of late 2025, a handful of open-weight models stand out as top performers for general-purpose chat with agentic capabilities. Agentic use here refers to models adept at complex tool use, multi-step reasoning (chain-of-thought), and coordinating sub-tasks or “subagents” autonomously. We focus on models that:

Exhibit high benchmark performance (nearly rivaling closed models like GPT-4) on recent leaderboards (e.g. LMSYS Chatbot Arena, ArenaHard, MT-Bench, HELM).

Are large-scale (favoring 30B+ parameters, including 70B and beyond) while noting a few standout smaller models when relevant.

Support extended context (many offer tens or even hundreds of thousands of tokens context, useful for long dialogues or documents).

Have permissive, commercially usable licenses (Apache 2.0, MIT, etc.), allowing self-hosting and enterprise use without restrictive terms
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Are optimized for tool use and “thinking” modes, meaning they can call external functions, APIs, or utilize plugins in a reliable way, often via structured function calling or special “reasoning” modes
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Run efficiently on modern hardware, with proven support in high-performance inference frameworks like vLLM and ability to scale from a single high-end GPU (like 1×H100 or the newer H200) up to multi-GPU servers (4× or 8× GPUs). We specifically note memory requirements and ideal RunPod configurations for each model.

Another important factor is community and ecosystem support. Models backed by active communities or major organizations tend to receive frequent updates, bugfixes, and better tooling (from fine-tuning adapters to inference optimizations). A strong ecosystem can be as vital as raw model quality
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Below we identify the leading open LLM candidates that meet these criteria. We detail their model specifications, unique strengths, license, benchmark performance, and recommended hardware setup for efficient inference. A comparative summary table is also provided.

Top Open-Source LLM Candidates (Dec 2025)
GLM 4.6 (Zhipu AI) – 355B MoE (32B active)

Overview: GLM-4.6 is a frontier-scale open model released by Zhipu AI under the MIT license
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. It uses a Mixture-of-Experts (MoE) transformer with 355 billion total parameters (roughly 32B active per token)
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. GLM-4.6 is essentially an open competitor to GPT-4-class systems, excelling in reasoning and coding tasks. It introduced a “thinking mode” for multi-step reasoning and improved planning, and it’s explicitly optimized for tool use: it can decide when to invoke external functions via native function-call support
clarifai.com
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. The context window was expanded to 200k tokens, enabling analysis of very large documents or codebases in one pass
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Performance: GLM-4.6 shows state-of-the-art open performance across reasoning and coding benchmarks. It consistently improved over its predecessor (GLM-4.5) and in evaluations came close to proprietary models like Anthropic’s Claude 4 on coding and multi-step tasks
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. For example, on coding benchmarks it achieves near parity with top closed models while using fewer tokens, and it demonstrates high success rates in tool-augmented reasoning
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. In Arena evaluations, GLM-4.6 has been rated among the very best open models (often in the top 3-5 on LMSYS Chatbot Arena). It typically scores in the mid-80s on knowledge tests like MMLU and can surpass 80% on complex reasoning benchmarks
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 (e.g. ~85% on MMLU and ~82% on GSM8K math in one comparison). In LMSYS’s MT-Bench (multi-turn chat quality), GLM-based models also scored above 8/10, indicating strong instruction-following and conversational ability.

 

Agentic Strengths: Thanks to its training on reasoning and code, GLM-4.6 is adept at autonomous tool use. It can generate chains-of-thought internally and decide to call tools/Apis when needed
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. This makes it effective as a central “brain” for agents – it can plan multi-step tasks, call external APIs, validate outputs, and iterate. Notably, GLM-4.6’s reliability in function calling is highlighted by its creators: it was fine-tuned to improve tool-call success and reduce errors in multi-tool workflows
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. It also supports function calling syntax out-of-the-box, which simplifies integration into agent frameworks.

 

Maintenance & Community: Zhipu’s GLM series has a robust open-source presence. GLM-4.6 was released openly in late 2025 with both base and instruct weights available
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. Its permissive license and strong performance have led to rapid adoption; community contributors have produced 4-bit and 8-bit quantizations and integrated GLM-4.6 with popular frameworks (Transformers, vLLM, SGLang, etc.)
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. There is active discussion and support on forums for fine-tuning GLM and deploying it, indicating healthy ecosystem engagement.

 

Inference & Hardware: Although GLM-4.6 is huge, the MoE design means only ~32B parameters are active at once. In practice, this model can be run on a single high-memory GPU with heavy quantization – community 4-bit compressions exist
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. For example, a single 80GB H100 can host 32B active params in 16-bit (≈64 GB) or comfortably in 8-bit/4-bit. However, to utilize the full model (all experts) for throughput, a multi-GPU setup is ideal. Recommended: 4× to 8× GPUs (A100/H100 80GB) for production use. Zhipu provides FP16/BF16 weights (which require ~500+ GB memory for all experts) and notes that 4-bit or 8-bit quantized deployments are supported to halve the GPU count
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. Using vLLM is highly advised; it can stream in experts on the fly and handle the large context efficiently. In practice, 8×80GB GPUs (e.g. an 8×H100 node) running vLLM can serve GLM-4.6 at ~70 tokens/sec single-stream throughput
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. With FP8 compression, even 4×H100 can achieve similar throughput at lower cost
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. Developers have also run GLM-4.6 on smaller setups (e.g. 2×H100) by offloading some weights to CPU, though at a cost of latency
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. In summary, GLM-4.6 is compatible with 1×GPU for R&D (using quantization and swapping) but shines with 4–8 GPUs for production. It is fully supported by vLLM and other optimized backends, making deployment feasible across RunPod configurations.

Qwen 3 (Alibaba) – 235B MoE (22B active)

Overview: Qwen-3 is Alibaba Cloud’s latest open-source LLM, released under Apache 2.0. It represents a major leap in scale and capability for Alibaba’s Qwen series (the earlier Qwen-14B was released in 2023). Qwen3 uses a Mixture-of-Experts with 128 experts, totaling 235B parameters, with about 22B active at a time
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. Notably, Qwen3 comes in both Instruct (chat-optimized) and Thinking (reasoning-optimized) variants
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. It also has a multimodal vision-language version (Qwen3-VL), though here we focus on the text model. Qwen3 is built for agentic tasks: it natively supports an “MCP” tool use protocol and was designed to easily integrate external tool calls
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. It boasts an enormous 256K context window (expandable to 1M with external indexing)
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, enabling extremely long conversations or analysis of lengthy content.

 

Performance: In late 2025, Qwen3 is considered perhaps the strongest open model overall. Updates in November 2025 (the “2507” Qwen3-Instruct checkpoint) yielded large gains across benchmarks
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. For example, Qwen3’s MMLU-Pro score jumped to 83.0 (up from 75)
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, and its performance on tough reasoning tests like AIME25 and ARC-AGI more than doubled versus prior versions
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. On coding, the LiveCode benchmark improved from ~33 to 51.8
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, indicating strong code generation and agentic coding abilities. Critically, independent evaluations show Qwen3 surpassing other open models: On LMSYS Chatbot Arena and ArenaHard, Qwen3’s Elo ratings are at the top. In fact, a recent analysis highlighted Qwen3-235B outperforming both Moonshot’s Kimi K2 and Anthropic’s Claude 4.5 (thinking mode) on a suite of hard benchmarks
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. One AI evaluator noted Qwen3 is “even more powerful than Kimi K2… and better than Claude Opus 4” on tasks like GPQA (general problem solving), AIME, and Arena Hard tests
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. This suggests Qwen3 has effectively taken the crown as of Dec 2025 for open models in broad capabilities. Its chat quality is also excellent – users report that Qwen3 follows instructions closely (after Alibaba moved away from a hybrid reasoning approach to separate instruct vs. thinking models for consistency
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). In MT-Bench, Qwen models score in the high 8s to low 9s out of 10, approaching GPT-4 level chat quality. And on crowd-sourced Arena battles, Qwen3-based chatbots achieve top-tier Elo (roughly 1420–1450 range, only a few dozen Elo behind GPT-4 variants)
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Agentic Strengths: Qwen3 was explicitly built with agent use-cases in mind. It supports structured function calling and tool APIs as first-class features. Alibaba introduced a framework called Qwen-Agent alongside Qwen3 – a lightweight toolkit to handle tool invocation and multi-step task execution with the model
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. The model itself has “strong agentic reasoning,” meaning it not only can call tools, but plan complex sequences of actions. For instance, Qwen’s documentation highlights a “Visual Agent” ability to operate GUIs (in the VL version) and a standardized protocol (MCP) for external tool calls
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. It can handle decisions on when to enter a “thinking mode” vs. respond immediately, although as of the latest version Alibaba split those modes into separate model checkpoints for clarity
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. In practical terms, Qwen3 can be toggled between a fast responding mode and a deep reasoning mode (the latter engaging chain-of-thought). This flexibility makes it powerful for building autonomous agents: simpler queries get quick answers, harder tasks trigger multi-step reflection (either via a special command or using the Thinking model variant). Its tool-use is robust – Qwen3 has high success on benchmarks like BFCL (tool use) and TAU multi-step decision tasks, indicating it can carry out multi-hop plans that were previously the domain of dedicated agent frameworks
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. It’s effectively an LLM+agent in one, with very few compromises in capability.

 

Maintenance & Community: Alibaba has been actively maintaining Qwen. The Qwen3 release (late 2025) was fully open-sourced (weights on HuggingFace) with Apache-2.0 license
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. This permissive licensing explicitly allows unrestricted commercial use, which is a boon for enterprise adoption
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. The community around Qwen has grown – by 2025, Qwen’s repositories were among the fastest-growing on Hugging Face, and usage spiked after the 2.5 and 3.0 releases
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. There is strong documentation (model cards, an official Qwen website) and engagement from Alibaba’s team (they’ve been responsive to issues and even provided quantized checkpoints like an FP8 version). With big tech backing and open governance, Qwen3 is likely to remain a top-tier open model with long-term support.

 

Inference & Hardware: Qwen3’s size demands significant hardware, but Alibaba has thoughtfully provided optimized versions. The standard BF16 model (235B) would normally require ~640 GB GPU memory for full loading
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 – roughly 8×80GB GPUs (they used 8×H100-80GB in training/inference)
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. However, Qwen3 comes with an FP8-quantized build, cutting memory in half and enabling 4×H100-80GB to run it comfortably
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. In fact, Alibaba’s benchmarking showed 4 H100s with FP8 can achieve ~72 tokens/sec, nearly matching the 8-GPU BF16 speed
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. The model can even scale down to 2×H100 with aggressive CPU offloading (community members have reported runs using ~143 GB across 2 cards)
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, though throughput will drop and latency will increase. For most users, we recommend 4× high-memory GPUs (>=80GB each) using FP8 or 4-bit precision as the sweet spot to host Qwen3-Instruct. This configuration is supported on RunPod’s multi-GPU instances and has been tested to deliver strong performance. Notably, vLLM and Alibaba’s own SGLang library both support Qwen3, including its massive context. vLLM’s efficient paging means even if the full 256K context is used, the model can stream outputs without running out of memory, by swapping parts of the context to CPU as needed. 1×H100/H200 use: A single H100 80GB can partially run Qwen3 if heavily quantized (e.g. 4-bit) and if one is willing to stream weights from CPU. But this is more of a proof-of-concept – interactive use would be very slow. Practically, to use Qwen3 for real workloads, plan on multi-GPU. On the upside, scaling out is linear and Tensor Parallel is supported (Alibaba demonstrated TP-4 and TP-8 configurations)
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. Also, Qwen3 offers dense smaller variants (down to 0.6B, 32B etc.) that one can use for development or to scale down if needed
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, with the option to switch to the big MoE when more power is required.

 

RunPod Note: All Qwen3 model versions (including the FP8) should run on the 8×A100 or 8×H100 offerings. For a 4×GPU setup, ensure the instance has sufficient CPU RAM and fast disk for offloading (as ~200+ GB of weights need to be accessible). Alibaba’s results suggest even 4×H200 (the next-gen Hopper) would suffice at FP8 with high throughput
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. H200 GPUs reportedly have more memory and faster interconnect, making them ideal for Qwen3’s heavy loads. In summary, Qwen3 is enterprise-ready: it demands ample hardware, but with FP8/4-bit optimizations and vLLM, it can be deployed on a variety of multi-GPU configurations.

Kimi K2 (Moonshot AI) – 1.04T MoE (32B active)

Overview: Kimi K2 – Thinking is an open LLM released by Moonshot AI (a Beijing-based lab) under a modified MIT license
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. It is remarkable for its scale: 1.04 trillion parameters total, with 32B activated per token
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. This makes Kimi K2 one of the largest MoE LLMs publicly available. The model was explicitly designed as an “open agentic intelligence”, meaning it’s trained to think step-by-step and use tools as needed
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. Kimi K2 introduced cutting-edge features like native INT4 quantization and an ultralong 256K context window, all while maintaining stable performance over extremely lengthy reasoning chains
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. The “Thinking” variant of Kimi K2 is tuned to produce chain-of-thought and to interleave reasoning with action (tool calls), enabling autonomous multi-step workflows.

 

Performance: Prior to Qwen3’s latest release, Kimi K2 was often cited as the best open model on several hard benchmarks. It reportedly set state-of-the-art scores on exams like Humanity’s Last Exam (HLE) and challenging agentic tasks like BrowseComp
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. Kimi’s strong suit is complex reasoning: it dramatically scales to hundreds of reasoning steps and doesn’t “drift” or lose context as earlier models would
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. In benchmark comparisons (from the K2 paper), Kimi K2 nearly matched or exceeded frontier proprietary models in heavy reasoning scenarios. For example, on HLE with tool use, K2 scored ~44.9% vs Claude 4.5’s ~41.7%
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; on an advanced math test (AIME25 with Python assistance) it hit 99–100%, essentially reaching GPT-5 level performance
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. These are niche academic benchmarks, but they illustrate Kimi’s capacity for deep problem solving when allowed to think in depth. On general tasks, K2 Thinking is slightly behind the very latest Qwen3 or GLM in raw accuracy – e.g. its MMLU-Pro is ~84.6%
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 (versus Qwen3 ~83 and GLM-4.6 ~87 on similar metrics), and its HumanEval (coding) pass@1 was high as well (Moonshot didn’t quote directly, but third-party evals put it in the mid-80s%). Chatbot Arena rankings placed Kimi K2 at or near the top through much of 2025; one community ArenaHard evaluation had K2 only a few Elo points shy of GPT-4
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. However, by December, as noted, Qwen3’s instruct model slightly overtook Kimi K2 on a few key benchmarks
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. Still, K2 remains extremely capable, especially for tasks that involve lengthy reasoning or heavy tool usage where it was purpose-built to excel. Its MT-Bench conversational score is around 8.5–8.7 (very good), though perhaps a tad more “technical” in tone compared to something like Llama. Users have noted that Kimi K2’s answers can be terse and analytical – great for engineering or scientific queries, but sometimes requiring prompting for more creative flair.

 

Agentic Strengths: This is where Kimi K2 shines brightest. It was trained to be an autonomous agent. Tool use and orchestration are baked into its training: Kimi can dynamically call functions, use a browser, write code, etc., all while maintaining an internal chain-of-thought. Impressively, it was shown to handle 200–300 sequential tool calls without losing coherence or going off-track
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. Earlier models like GPT-4 (via ReAct prompting) would often degrade after maybe 30+ tool uses; Kimi K2 holds a goal in mind across hundreds of steps, reflecting a new level of long-horizon agency
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. Its chain-of-thought is end-to-end trained, meaning it isn’t just role-playing reasoning – it truly was optimized to reason and act in tandem. The model card explicitly says “deep thinking & tool orchestration” is a key feature
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. For example, K2 will output a thought process, decide to invoke a search or calculator tool (using a special token format), get the result, continue reasoning, and finally produce an answer. This makes it ideal for building complex autonomous systems (e.g. research assistants, AutoGPT-style agents) without needing separate planning modules. Additionally, Kimi’s long-term consistency in these modes is industry-leading – it mitigates the “forgetfulness” issue over long dialogues by having a stable chain-of-thought vector and maybe specialized attention mechanisms to handle 256k context without derailing. In summary, K2 is arguably the most “agent-like” of the open models, able to carry out multi-step plans reliably where others might falter.

 

Maintenance & Community: Moonshot AI open-sourced Kimi K2 in late 2025. The license is “Modified MIT,” which is mostly permissive but with a clause clarifying that model outputs (synthetic data it generates) are not considered part of the model for the purpose of the license
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 – essentially to avoid any IP ambiguity when using its outputs. This is still a commercially friendly stance (you can use Kimi freely in products). Being a newer entrant, Kimi’s community is smaller than Llama’s or Qwen’s, but it has garnered attention. Its Hugging Face model has over a thousand likes, and early adopters in the research community have praised it as a major breakthrough
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. Moonshot published a detailed technical report (on arXiv) describing K2’s architecture and training
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, which adds credibility and understanding for developers. The model comes with an extensive model card including example usage for chat and tool calling
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. The community has produced some quantized versions and integration code (e.g., in LangChain). Given Moonshot’s base in China and the model’s prowess, there is a growing user base in the Chinese AI circles as well. We anticipate continued updates (perhaps Kimi K3 in the future) and improvements as Moonshot competes in the open model space.

 

Inference & Hardware: Running a 1 trillion parameter model might sound daunting, but Kimi K2’s native INT4 quantization greatly reduces the burden. The model was quantization-aware trained to use 4-bit weights with minimal quality loss
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. In effect, that cuts memory needs by 4× compared to FP16. The 32B active parameters therefore can fit in ~16 GB at runtime (32B × 4 bits ≈ 16 GB) – easily within a single high-end GPU memory. The challenge is the total weights (the experts): ~1.04T params at 4-bit is on the order of 500 GB. In practice, these can be sharded across multiple GPUs. Optimal setup: 8×80GB GPUs (H100 or A100) to fully load all experts in parallel (each GPU would host ~125 GB worth of weights, which fits on 80GB with some CPU offload and the fact not all experts are in VRAM at once). In an 8-GPU setup, Kimi’s MoE can be distributed such that each GPU handles a portion of the experts, and the model can route tokens to experts across GPUs. This is analogous to how Qwen3 was run, and indeed likely 8×H100 was used to achieve the SOTA benchmark runs for K2. With vLLM or a custom runtime, one could also run Kimi on fewer GPUs by swapping expert weights in and out as needed. For instance, a 4×H100 system could handle K2 with some performance hit – it might need to load experts from CPU when an “off-partition” expert is required. Since K2 activates 8 experts per token
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, a reasonable approach is to assign 8 expert groups to 4 GPUs (i.e. 2 experts per token per GPU), which doubles the load per GPU relative to an 8-way split. Community feedback suggests this is possible but reduces generation speed. The model card also notes that K2 was tested in “low-latency mode” thanks to the INT4 speedup
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, meaning it can generate faster than typical for its size. Empirically, users have reported K2’s throughput to be on par with or even better than a dense 70B model, when running on sufficient hardware, due to the MoE parallelism and INT4.

 

On single GPU scenarios, running Kimi K2 is mostly for experimentation. You could load just the 32B activated weights in 4-bit (which fits on one 24GB GPU) and have the experts on CPU, but inference would be very slow as each token might thrash loading many experts from RAM. It’s not recommended for real-time use. If you have at least 2×80GB GPUs, you could attempt a partial load (Moonshot hasn’t published exact 2-GPU configs, but based on Qwen’s data, ~2 GPUs with offload could handle it at slow speed
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). For practical deployment, we suggest Kimi K2 for those who have access to 4–8 GPU servers. On RunPod, an 8×A100 80GB instance would be a target setup; K2’s INT4 means 8×A100 should handle it with room to spare (since 8×A100 gives 640GB total VRAM, more than enough for ~500GB of 4-bit weights, potentially even 6×GPUs might suffice with some overflow to CPU). The model supports TensorParallel and pipeline parallelism, and works with HuggingFace Transformers (with custom code for MoE) as well as vLLM (if using a vLLM version that supports MoE Sharded models – Mistral and Alibaba contributed such features which may also work for K2). Also noteworthy: Kimi’s 256K context is fully supported in its architecture; ensure to use flash attention or optimized attention kernels for efficiency at that length. Memory usage scales with context, so long prompts will eat more VRAM – another reason to have multiple GPUs. The bottom line: Kimi K2 can run on similar hardware as Qwen3 or Mistral Large, and thanks to 4-bit quantization, it can be surprisingly memory-efficient per GPU. Just plan for a multi-GPU setup to leverage its full power.

Mistral Large 3 (Mistral AI) – 675B MoE (41B active)

Overview: Mistral Large 3 is the flagship model from Mistral AI’s third-generation release (announced late 2024)
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. It is a sparse MoE model with 41B active parameters and 675B total (i.e. many experts)
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. Mistral 3 is notable for being multimodal and multilingual out-of-the-box – the base model has learned from text and images, and handles multiple languages at high proficiency
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. All Mistral 3 models, including Large 3, are released under the Apache 2.0 license, making them fully open for commercial use
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. Mistral Large 3 comes in a base version and an instruction-tuned version (with a reasoning-optimized variant promised as well)
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. It was trained on 3,000 NVIDIA H200 GPUs, highlighting the scale of the project
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. Mistral frames this model as a frontier “open-weight” model, aiming for parity with the best instruction-tuned open models on general tasks
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. They also emphasize multilingual chat and image understanding as strengths – in fact, Mistral Large 3 is among the best open models for non-English conversation quality and even has some degree of image input capability (though not as advanced as specialized vision models).

 

Performance: Upon release, Mistral Large 3 debuted strongly on leaderboards. It ranked #2 among permissively licensed open models (for non-“reasoning” category) and #6 overall when including research-only models on LMArena’s charts
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. In practical terms, it is competitive with Meta’s Llama 3 70B and other 70B+ class models. For example, anecdotal benchmarks put Mistral Large 3’s MMLU around 82-83%, GSM8K math in high 70s, and MT-Bench ~8.0 (slightly behind the best 70B dense models)
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. Where it punches above its weight is multilingual tasks – Mistral reported best-in-class performance in multilingual dialogue for its size
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. And thanks to vision pretraining, it can interpret images in prompts (e.g. describing an image or reasoning about visual input) better than many text-only models. Mistral Large 3’s coding ability is solid though not top of the pack; its smaller sibling “Mistral 7B (v0.1)” made waves in mid-2023 for strong performance, and Large 3 continues the trend at a higher scale. It may lag slightly behind specialized coding models like DeepSeek or WizardCoder on pure code tests, but it is a balanced generalist. Another highlight: Mistral Large 3 has very good efficiency – it tends to generate answers with fewer tokens (more concise) while maintaining accuracy
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. This means for tasks like summarization or Q&A, it might finish responses faster (in token count) than some verbose models, which is a practical advantage in throughput.

 

In summary, while Mistral Large 3 might not beat the absolute top model (like Qwen3 or GLM-4.6) on every benchmark, it is definitely among the elite open models. It provides a strong open alternative, especially if one values multilingual and multimodal capabilities. It’s also actively improving (a reasoning-optimized version was hinted), so its performance is likely to rise further.

 

Agentic Strengths: Mistral Large 3 was not explicitly described as an “agent” model in the way Kimi or Qwen were, but it still supports complex tool use. It has the advantage of being multimodal, so it can potentially act on visual inputs (imagine an agent that can see an interface or images and reason). The instruction-tuned variant is capable of typical tool-using prompts (e.g., following ReAct style cues or function call formats). Additionally, Mistral’s training presumably included some of the Mixtral series knowledge (their earlier MoE “Mixtral” models) which were geared towards efficiency and reasoning. Large 3’s long context (128K tokens) also helps in agentic scenarios where the model can keep a large scratchpad or memory of previous tool interactions. It may not have a dedicated chain-of-thought mode, but one can prompt it to produce one. Given Mistral’s close work with vLLM and others, it likely supports function calling via libraries. In the absence of an official “thinking mode”, we consider Mistral Large 3 as a very capable general model that can be applied to agentic use-cases with the right prompting or minor fine-tunes.

 

One noteworthy aspect is multilingual tool use: Mistral Large 3, being strong in non-English, could power agents that operate in other languages (something many English-centric models struggle with). If your use-case involves an agent conversing or acting in, say, French or Arabic, Mistral might have an edge due to its training diversity
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.

 

Maintenance & Community: Mistral AI has emerged as a serious open-source player (they released Mistral 7B in 2023, which was widely adopted). With Mistral 3, they show commitment to open releases: all models from 3B up to Large 3 are Apache-2.0 licensed
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, which is as open as it gets. They also provided models in various optimized formats (they mention releasing “compressed formats” like NVFP4 for Large 3) to ease deployment
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. Mistral collaborated with industry partners (NVIDIA, Red Hat) to ensure good support for their models in software stacks
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. This means when you choose Mistral, you benefit from official integration with things like TensorRT-LLM and vLLM. Community reception has been positive; Large 3 is recognized for bringing a true “frontier” model to open source. As a result, there’s strong community support in terms of model hubs, inference scripts, and fine-tuning projects building on Mistral. We can expect ongoing refinements (the mention of a coming “reasoning version” suggests an update or variant might be released to further boost performance on complex tasks).

 

Inference & Hardware: Mistral Large 3 was explicitly optimized for easier deployment despite its scale. The model is available in NVFP4 format (an 4-bit weight format developed with NVIDIA) which reduces memory usage significantly
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. The Mistral team states that Large 3 can be run efficiently on a single 8×A100 or 8×H100 node using vLLM
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. In fact, they mention testing on Blackwell NVL72 systems (which likely refers to NVIDIA’s DGX Blackwell platform in development) and achieving good results
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. For today’s hardware, 8 GPUs is recommended to host Mistral Large 3 for full speed. Each GPU (80GB) would handle roughly 85 GB of weights in FP16, but with FP4 compression it’s far less – well within 80GB. So 8×80GB gives plenty of headroom. It’s likely possible to run on 4×80GB GPUs as well, with some trade-offs in throughput (since each GPU must cover more experts). The model’s active size (41B) means even 1×GPU could load the working set if quantized (41B at 4-bit ≈ 20.5 GB, so one 24GB GPU could manage that). The real question is handling the experts (~675B total). With 8 GPUs it’s comfortable; with 4 GPUs, vLLM’s paging could help by keeping some experts in CPU memory. RunPod scenarios: On a 4×H100 (80GB) machine, Mistral Large 3 should run with vLLM in NVFP4, possibly needing high-bandwidth CPU NVMe for any overflow. It may not hit max throughput but would still function for moderate loads. On 8×A100/H100, you can expect near-optimal throughput (Large 3 was reported to generate ~126 tokens/s in one config, though that might have been an earlier version)
vellum.ai
vellum.ai
. Importantly, Mistral’s partnership with NVIDIA means TensorRT-LLM support: if you convert the model to a TensorRT engine (with 8-bit or 4-bit), you can get very fast inference leveraging GPU tensor cores. This might yield lower latency per token.

 

Mistral Large 3 also supports speculative decoding and disaggregated serving (prefill on one set of GPUs, decode on another) as mentioned in their release blog, all aimed at speeding up inference on long contexts
mistral.ai
. These advanced techniques will interest those deploying at scale, but the takeaway is that Mistral is highly optimized for deployment. For a single GPU user, running the smaller “Ministral 14B/8B/3B” models is a better bet – those are part of the same family and cover lightweight scenarios with excellent cost-performance
mistral.ai
mistral.ai
. In fact, Ministral 14B (the 14B dense model) is noted to reach 85% on AIME’25 math in its reasoning mode
mistral.ai
, showing the efficiency of the smaller models. These smaller ones can easily run on 1×H100 or even 1×RTX 4090 with quantization.

 

In conclusion, Mistral Large 3 offers a great blend of performance and deployability. It is supported on all common frameworks and hardware setups; for full utilization, stick to 8×GPU clusters, but for development you can slice it down to fewer GPUs with the right optimizations.

Meta LLaMA 2 and 3 (Meta AI) – 70B Dense Models

Overview: No discussion of open LLMs is complete without the LLaMA lineage. Meta’s LLaMA 2 (released July 2023) and its successor LLaMA 3 (released in 2024) form the backbone of many fine-tuned chat models in the community. While these models are outperformed by the newer MoE giants above, they remain very relevant due to their robust general-purpose capability, wide adoption, and extensive community fine-tunes. LLaMA 2 was introduced as a 7B–70B family, with the 70B variant being the best. It is licensed for free use (including commercial, with some usage guidelines). LLaMA 3 further improved on this, expanding context length (8K+ tokens) and sharpening instruction-following
blog.galaxy.ai
blog.galaxy.ai
. Notably, LLaMA 3 70B Instruct was available on Hugging Face by mid-2024
blog.galaxy.ai
, and Meta reportedly experimented with an even larger 405B version internally (though that was not broadly released). LLaMA models are dense Transformer models (no MoE), which makes them simpler to deploy (fewer sharding complexities) but also means quality is directly tied to parameter count and training. The 70B LLaMA 2 or 3 is roughly the strongest dense open model until perhaps Falcon-180B or others come along (Falcon 180B, if released, would be another candidate, but here we focus on Meta’s due to popularity and support).

 

Performance: LLaMA 2 70B was in 2023 considered nearly GPT-3.5 level on many tasks. LLaMA 3 70B (“Llama 3.3” in some references) narrowed the gap further. For instance, one independent test found LLaMA 3.3 70B scored ~86% MMLU, ~81% GSM8K, and had an MT-Bench score of 9.0
skywork.ai
 – solid improvements, putting it just a notch below the likes of Qwen 72B. It tends to handle knowledge and common-sense reasoning well. LLaMA 2/3’s weaknesses are in extremely complex reasoning (where MoE models now pull ahead) and some coding tasks (where specialized fine-tunes do better). On the LMSYS Chatbot Arena, LLaMA 70B-based models like Vicuna-33B/70B or Meta’s own LLaMA-Chat have been strong contenders, though recent entrants have edged them out. Still, LLaMA 70B remains a workhorse: it’s very capable at general chat, creative writing, Q&A, etc. Its responses are usually coherent and helpfully detailed (especially with instruction fine-tuning). Meta’s fine-tuned chat versions (Llama-2-Chat, etc.) were aligned for helpfulness and safety, making them reliable for broad use. The Llama ecosystem also enabled many derivative models – e.g., Vicuna, WizardLM, Alpaca, XWinLM, OpenAssistant – which built on LLaMA weights to optimize for conversation or specific domains. These derivatives often appear in “Top N models” lists because they can be as good as the base model or slightly better for certain tasks. However, as of 2025, the pure Meta-released LLaMA 3 70B Instruct is generally surpassed by the newer large MoE models in benchmarks
skywork.ai
. The trade-off is that LLaMA is lighter and sometimes faster per token (since 70B dense uses less compute than 32B active MoE with overhead). Thus, for some, a well-tuned LLaMA 70B may be “good enough” with less system complexity.

 

Agentic Strengths: Out-of-the-box, LLaMA 2/3 chat models were not explicitly built for tool use (Meta’s training didn’t include tools or function calling beyond perhaps some JSON format understanding). But the community addressed this: there are fine-tunes like LLaMA-Adapter, ToolLLaMA, Gorilla, etc., and frameworks that wrap LLaMA with decision-making logic. Moreover, LLaMA 3 introduced or supported features like function calling and structured outputs in its instruct model
blog.galaxy.ai
blog.galaxy.ai
. Users noted LLaMA 3 would reliably format outputs when asked (e.g. bullet lists, JSON), which is crucial for tool integration
skywork.ai
. It can also be guided to call APIs if given the right prompting schema (for example, meta-prompts that instruct it to produce a <tool> action). While LLaMA might not be as inherently agentic as Kimi or Qwen, it is certainly capable of multi-step reasoning – especially if you prompt it with a “Let’s think step by step” approach. Many AutoGPT-style systems in 2023/2024 actually used a LLaMA variant under the hood to do the planning and tool use. In terms of coordination, LLaMA can maintain context over reasonably long chains (8K context is decent, though shorter than the 100K+ some others offer). It might need more careful prompt management to avoid forgetting earlier steps compared to something like Kimi which was trained specifically for long tool chains. Nonetheless, with the huge community, there are off-the-shelf agent wrappers for LLaMA. In particular, projects like HuggingGPT and LangChain have LLaMA integrations for tool usage. The big plus: if your tools and ecosystem already support LLaMA formats, using LLaMA 70B ensures broad compatibility.

 

Maintenance & Community: This is where LLaMA shines. Meta’s releases galvanized the open LLM community. The ecosystem around LLaMA is arguably the largest – tons of third-party libraries, fine-tune checkpoints, and active forums
skywork.ai
. While Meta itself may update the model only occasionally (LLaMA 3 being the last major release publicly known), the community keeps it relevant via fine-tunes (for instance, WizardLM 2.0 70B came out in 2024 enhancing reasoning, Vicuna 1.5 improved conversational quality, etc.). LLaMA’s format has become a de-facto standard for many tools, so using a LLaMA-derived model often means easy integration (with UCS compatibility, prompt templates widely shared, etc.). Meta’s license for LLaMA 2 and 3, though custom, allows commercial use (with some restrictions against using the model to train larger models, and a request for responsible use), so practically it’s been adopted in numerous startups and products. The community also provides continuous support in the form of prompt engineering tips, safety mitigations, and performance benchmarking for LLaMA variants.

 

Inference & Hardware: LLaMA 70B, being dense, requires about ~140 GB in FP16. Common practice is to use 8-bit or 4-bit quantization to shrink that. For instance, in 4-bit (GPTQ or similar), the model can fit in roughly 35–40 GB. This means a single 48GB GPU can host a 70B model in 4-bit with some room, making LLaMA 70B one of the largest models you can run on one GPU alone. Indeed, many hobbyists ran LLaMA2-70B on a single RTX 4090 (24GB) by using 4-bit and CPU offloading half the layers. For better performance, 2× 24GB GPUs or 1× 80GB GPU is recommended (80GB H100 can load 70B in 8-bit, or even 16-bit with slight paging). On RunPod, a single H100-80GB instance is a great option for LLaMA 70B. It will run relatively fast (faster than the MoEs per token). If more throughput is needed, LLaMA also supports Tensor Parallel across GPUs – e.g. 2×40GB GPUs could split the model, or 4× GPUs at lower memory. Because it’s dense, scaling beyond 2–4 GPUs yields diminishing returns unless doing many concurrent threads (since you can’t parallelize a single inference beyond model parallelism). Usually 2×40GB (or 4× if 20GB each) is enough to serve multiple simultaneous chats with LLaMA. vLLM fully supports LLaMA models and can drastically improve multi-user throughput by sharing KV caches. In a vLLM scenario, even a single H100 can handle many concurrent prompt streams, returning first tokens in ~0.3–0.5 seconds
vellum.ai
vellum.ai
 and streaming thereafter, thanks to optimized scheduling.

 

Memory and context: LLaMA 3 increased context to 8K (and some unofficial fine-tunes push it to 32K with RoPE extrapolation). While not at Qwen’s 200K, 8K is sufficient for most chat sessions and medium-length documents. If needed, one can use context stuffing or RAG (Retrieval-Augmented Generation) with LLaMA to overcome the window limit. Many tools exist to do this given LLaMA’s popularity. Additionally, LLaMA’s inference footprint per token is moderate – at 70B it might output ~20 tokens/sec on an H100 in 8-bit, which is decent. Summing up, LLaMA 2/3 70B is the most accessible “large” model for those with limited hardware, and it remains a strong baseline for general chat. It may not lead every benchmark now, but it’s battle-tested and easy to deploy broadly.

Comparison of Top Open LLMs

The table below summarizes the key attributes of the top models discussed, to facilitate quick comparison:

Model (Size)	License	Notable Strengths	Agentic Features	Recommended Hardware
GLM 4.6 (MoE 355B → 32B active) 
clarifai.com
clarifai.com
	MIT (permissive) 
clarifai.com
clarifai.com
	- Frontier-scale Mixture-of-Experts model (200k context)
- Top-tier reasoning & coding (near GPT-4 level on many tasks)
clarifai.com

- “Thinking” mode for multi-step logic
clarifai.com

- High reliability and correctness in answers	- Native tool use & function calling support (decides when to call APIs)
clarifai.com
clarifai.com

- Designed as an autonomous agent core (internal CoT, multi-step planning)
clarifai.com
	- 8×80GB GPUs for full throughput (FP16)
- 4×GPUs with FP8/4-bit for cost-efficient serving
venturebeat.com
venturebeat.com

- Possible on 1×GPU with heavy quantization (low speed)
Qwen 3 (MoE 235B → 22B active) 
venturebeat.com
	Apache 2.0 
huggingface.co
venturebeat.com
	- Latest Alibaba model, state-of-the-art open-chat performance (outperforms most peers)
venturebeat.com

- 256K context + multimodal (VL variant)
huggingface.co

- Strong across domains: math, code, multilingual, factual QA	- Built for agent orchestration (Alibaba’s Qwen-Agent framework)
venturebeat.com

- Supports standardized tool API calls (MCP protocol)
qwen-3.com

- Separate Thinking vs Instruct modes for reasoning on demand
venturebeat.com
venturebeat.com
	- 8×80GB GPUs (BF16) or 4×80GB (FP8) for production
venturebeat.com
venturebeat.com

- Can run on 2×80GB with offloading (reduced speed)
venturebeat.com
venturebeat.com

- Fully supported in vLLM and TensorParallel
Kimi K2 (MoE 1.04T → 32B active) 
huggingface.co
	Modified MIT 
huggingface.co
 (commercial use allowed)	- Deep reasoning champion (tops many hard benchmarks)
huggingface.co

- End-to-end trained for long chains of thought (stable over hundreds of steps)
huggingface.co

- Native INT4 quantized weights (fast and memory-efficient)
huggingface.co
huggingface.co

- 256K context window	- Exceptional tool use capability: can intermix reasoning and tool calls fluidly
huggingface.co

- Maintains goal-oriented coherence over 200+ tool invocations
huggingface.co

- Essentially an autonomous agent out-of-box (requires minimal prompting for tools)	- 8×80GB GPUs recommended (int4 weights ≈500 GB total) for full performance
- 4×GPUs feasible with MoE sharding + offload (INT4 reduces VRAM per GPU)
- Not ideal for 1×GPU except testing (active 32B fits, but experts would stream from CPU)
Mistral Large 3 (MoE 675B → 41B active) 
mistral.ai
	Apache 2.0 
mistral.ai
	- Multimodal (image+text) and multilingual mastery
mistral.ai

- Strong generalist: high instruction quality, concise outputs
mistral.ai

- Highly optimized (NVFP4 release, fast inference, low token redundancy)	- Good at tool use with proper prompting (not special-cased but capable)
- Long 128K context suits agent memory needs
mistral.ai

- Multilingual tool usage (effective in non-English tasks)	- 8×80GB GPUs (with 4-bit) for efficient vLLM serving
mistral.ai

- 4×80GB possible with NVIDIA TensorRT-LLM optimizations
mistral.ai

- Smaller Ministral (14B/8B) models available for 1×GPU scenarios
Meta LLaMA 2/3 (70B dense) 
blog.galaxy.ai
blog.galaxy.ai
	Meta license (permits commercial use with guidelines) 
blog.galaxy.ai
	- Extremely versatile general chatbot (strong dialogue, creativity, knowledge)
- Huge ecosystem of fine-tunes (e.g. Vicuna, etc.) and tooling support
skywork.ai

- LLaMA 3 adds 8K context and better following of instructions
skywork.ai
	- Supports function calling & structured output formats (LLaMA 3 instruct)
blog.galaxy.ai
skywork.ai

- Widely used in agent frameworks (LangChain, etc.), though not specialized for tools
- Reliable baseline for multi-turn conversations and moderate reasoning	- 1×80GB GPU can run 70B (8-bit or 4-bit quant.)
- 2×48GB GPUs for full 16-bit inference or higher throughput
- Excellent vLLM performance for multi-user serving (lighter weight than MoE models)

Table Notes: All these models support vLLM for efficient serving (streaming and paged attention), and all have some quantized form (4-bit or 8-bit) enabling lower VRAM use. “MoE X→Y active” denotes Mixture-of-Experts total vs activated parameter counts. Hardware recommendations assume batch-1 generation; for higher batch or longer contexts, additional memory headroom is beneficial. Licensing is permissive in all cases, with no non-commercial restrictions. Each model has official download links (e.g. HuggingFace repositories or company sites) where further inference instructions and compatibility notes are provided.

Performance Trade-offs and Deployment Considerations

When choosing among these top open LLMs, one should consider performance vs. resource trade-offs and the specific application needs:

Raw Chat & Knowledge Quality: If you need the absolute strongest chat performance and factual accuracy, larger MoE models like Qwen3 or GLM-4.6 are leading choices. They have outscored others on aggregate benchmarks and approach proprietary GPT-4 levels
venturebeat.com
clarifai.com
. However, they demand more GPUs and careful setup. If your use-case can tolerate slightly lower raw accuracy but you prefer simpler deployment, LLaMA 70B or Mistral Large 3 might suffice – they still perform very well (within ~5-10% of the leaders on many tests
skywork.ai
) but are easier to run on modest hardware.

Agentic Task Complexity: For building an autonomous agent (e.g. an AI that must plan a research task, use tools like web browsing, code execution, etc.), Kimi K2 offers unparalleled out-of-the-box support for long, complex tool sequences
huggingface.co
. It’s essentially pre-aligned for such multi-step workflows. Qwen3 also explicitly targets this scenario, especially with its separate reasoning mode and official agent framework
venturebeat.com
. GLM-4.6 has strong tool use capabilities too, though it may require more prompting to fully utilize its “thinking” mode. LLaMA-based models can be made into capable agents but typically require an external “agent loop” (they won’t autonomously decide to use a tool unless instructed to by the system or prompt design). In summary, for maximum autonomy and tool-use stability, Kimi K2 and Qwen3 are standouts, whereas LLaMA or Mistral can serve in agent roles with a bit more orchestration logic around them.

Model Size vs. Speed: Larger models (with more active parameters) generally give better quality but slower inference. MoE models mitigate this by parallelizing experts – for instance, Qwen3 (22B active) may decode faster per token than a 70B dense model, since only 22B worth of weights are used each step (and possibly spread over multiple GPUs). That said, MoEs often have overhead from gating and communication. In practice, a well-optimized MoE like Mistral 3 or Qwen3 can achieve high throughput (tens of tokens/sec) on multi-GPU nodes
venturebeat.com
vellum.ai
. Dense models like LLaMA 70B might be slower per token on a single GPU but can scale out somewhat. If latency is critical (e.g. responding in under 1 second), one might lean towards a smaller model or heavier quantization. Mistral’s smaller variants (3B/8B/14B) are excellent for fast, low-latency needs – they trade some accuracy for big speed-ups and can even run on CPU in real-time for 3B. On the other hand, if accuracy and capability trump all and some latency is acceptable, the largest models (Kimi with multi-step thinking or Qwen with its reasoning) will deliver superior results on hard tasks (like complex problem solving or code generation), at the cost of requiring more compute per query.

Memory and Context Length: All the listed models have extended contexts (≥ 128K for the MoEs, 8K for LLaMA by default). If you plan on very long documents or conversations, the MoE models have an edge. They were explicitly designed to handle long contexts (and tested up to their limits). LLaMA 70B can be fine-tuned or tweaked for longer contexts but with some degradation beyond 8K. If an application involves processing, say, a 200-page PDF in one go, GLM-4.6 or Qwen3 would be uniquely suited (200k tokens context)
clarifai.com
huggingface.co
. Keep in mind that using these maximum contexts will slow down any model and use a lot of memory – vLLM helps by swapping out past context pages, but there’s a throughput hit. One strategy is to use retrieval (vector databases) with models like LLaMA to simulate long context via smart prompt construction, which can be more efficient for some cases.

Hardware Optimizations: All these models benefit from modern GPU features. It’s advisable to use Nvidia H100 or A100 GPUs for their large memory and speed. The upcoming H200 GPUs (as referenced by Mistral using them for training
mistral.ai
) presumably offer more memory bandwidth and possibly larger memory (rumors suggest 100GB+ per card). That will further ease running big models on fewer GPUs. Many of these models also support FlashAttention and other memory-saving techniques out of the box (for example, Qwen’s HuggingFace code recommends enabling FlashAttention v2 for speed
huggingface.co
). When deploying, turning on such options can dramatically improve throughput and reduce memory usage. Some, like Mistral, also work with TensorRT-LLM for 8-bit inference acceleration
mistral.ai
 – using that can double token rates if integrated properly, which is great for real-time applications.

Ecosystem and Support: Finally, consider the level of community support needed. LLaMA-based models have the largest pool of developer knowledge and third-party integrations (from chat UIs to guardrail systems). If you need a proven stable platform with lots of plug-and-play components, LLaMA (or its fine-tunes like Vicuna) is a safe bet – it’s often the reference model in open-source chat applications. Qwen3 and Mistral 3 are catching up fast in community adoption, and have corporate backing (Alibaba and Mistral) ensuring ongoing support. Kimi K2 and GLM-4.6 are more niche by comparison but still actively maintained by their creators; they might just have slightly fewer ready-made tutorials or off-the-shelf integrations right now. However, given their performance, the extra effort to integrate them can be worthwhile for demanding use-cases.

In conclusion, December 2025 finds the open LLM arena richly populated with models that for the first time approach the capability of the best proprietary systems – and do so with permissive licenses. Whether one needs an “agentic AI” to run complex multi-tool workflows, or a reliable multilingual chatbot, or a coding assistant, there is an open model available. By choosing one of the top candidates above and pairing it with the appropriate hardware (from a single H100 to an 8×GPU server depending on scale), developers can deploy powerful AI systems without API restrictions. Moreover, these models are rapidly evolving, supported by vibrant communities and research, ensuring that open-source AI remains at the cutting edge going forward
skywork.ai
venturebeat.com
.

 

Sources: The information in this report is drawn from official model cards and technical blogs (Zhipu’s GLM-4.6 guide
clarifai.com
clarifai.com
, Alibaba’s Qwen3 release notes
venturebeat.com
venturebeat.com
, Moonshot’s Kimi K2 documentation
huggingface.co
huggingface.co
, and Mistral’s announcement
mistral.ai
mistral.ai
), as well as benchmark analyses and community evaluations (e.g. LMSYS Arena results and independent comparisons
venturebeat.com
skywork.ai
). Each model’s weight download link and further usage instructions can be found on their Hugging Face pages or official websites (referenced in the citations).
