Kimi K2.5 vs Qwen3.5-35B-A3B: Frontier Powerhouse Meets the Tiny Giant Killer

This is probably the most lopsided hardware matchup you will find in the open-weight space right now. Kimi K2.5 from Moonshot AI weighs in at 1 trillion total parameters with 32 billion active per token, 384 experts, and a multi-agent swarm system that can orchestrate up to 100 sub-agents. Qwen3.5-35B-A3B from Alibaba has 35 billion total parameters with 3 billion active, and it fits comfortably on a single consumer GPU with enough room to spare.

The benchmarks reflect that gap. K2.5 posts AIME 2025 at 96.1, SWE-bench Verified at 76.8%, and GPQA Diamond at 87.6. These are numbers that compete with the best proprietary models. Qwen3.5-35B-A3B, despite being 28x smaller in total parameters and activating roughly 10x fewer per token, managed to surpass the previous Qwen3-235B flagship across several benchmarks. That is a remarkable engineering achievement, but it still leaves a substantial gap against a model in K2.5's weight class.

The real question is whether you need that gap closed. For a lot of production workloads, you do not.

Quick Comparison

Kimi K2.5: The Open-Weight Frontier

Kimi K2.5 is Moonshot AI's answer to the question of how far you can push an open-weight model. The architecture is a 61-layer MoE with 384 experts, 8 active per token, producing 32 billion active parameters on each forward pass. That is already impressive, but the real differentiator is what sits on top: an Agent Swarm system trained with PARL (Process-Aware Reinforcement Learning) that can coordinate up to 100 sub-agents working in parallel.

The benchmark results are hard to argue with. AIME 2025 at 96.1 and HMMT at 95.4 put K2.5 at the very top of mathematical reasoning. SWE-bench Verified at 76.8% is among the best scores posted by any model. On BrowseComp, the Agent Swarm configuration scores 78.4%, compared to 60.6% in single-agent mode - a 17.8-point lift that demonstrates the practical value of the swarm architecture. For a full breakdown of K2.5's capabilities, see our Kimi K2.5 model page.

The vision system is another strength. MoonViT-3D is a 400M parameter vision encoder that handles native resolution images and video natively. On OCRBench, K2.5 scores 92.3, and on MMMU-Pro it hits 78.5 - numbers that put it in the top tier for multimodal reasoning. The model does not just read text from images; it reasons about visual content at a level that most vision-language models cannot match.

The trade-off is infrastructure. A trillion parameters, even as an MoE, demands serious hardware. You are looking at multi-node GPU deployments for self-hosting. The Moonshot API at $0.60/$3.00 per million tokens is not cheap, though it is reasonable for frontier-class performance. The Modified MIT license is permissive but not quite as straightforward as a standard MIT or Apache 2.0.

Qwen3.5-35B-A3B: The Single-GPU Revolution

The story of Qwen3.5-35B-A3B is fundamentally about what happens when you optimize architecture hard enough. Alibaba's Qwen team combined Gated Delta Networks with sparse MoE routing to create a model that activates only 3 billion parameters per token, yet outperforms the 235-billion-parameter Qwen3 flagship that preceded it. That is not incremental improvement. That is a generational leap in parameter efficiency.

At FP8 quantization, the full 35B model fits in roughly 18-20 GB of VRAM. An RTX 4090 handles it comfortably. An RTX 3090 can run it. Even high-end Apple Silicon with unified memory works well. This is a model you can run on hardware you might already own, with zero ongoing API costs, under the most permissive open-source license available.

The 262K context window matches K2.5's 256K, and with extended context techniques Qwen claims support for 1M+ tokens. Apache 2.0 licensing means zero restrictions on commercial use, fine-tuning, or redistribution. For teams comparing small efficient models, see our comparisons of Qwen3.5-35B-A3B vs GLM-4-7B-Flash and Qwen3.5-35B-A3B vs Nemotron 3 Nano.

The limitation is clear: raw benchmark scores cannot match a model that is 28x larger. On GPQA Diamond, SWE-bench, and MMLU-Pro, K2.5 holds double-digit leads. Qwen3.5-35B-A3B is punching above its weight, but there is a ceiling to how high 3 billion active parameters can reach on the hardest tasks.

Benchmark Comparison

The deltas are large, and they should be. K2.5 is activating 10x more parameters per token and drawing from a pool of 384 experts versus Qwen's much smaller expert set. The relevant metric is not who wins - it is whether Qwen's scores are good enough for your task. For many production applications, the answer is yes. For frontier research or the hardest reasoning challenges, K2.5 is in a different league. Check our reasoning benchmarks leaderboard and math olympiad leaderboard for broader context.

Kimi K2.5: Pros and Cons

Pros:

• AIME 2025 at 96.1 and HMMT at 95.4 - among the best math reasoning scores available
• Agent Swarm with PARL training orchestrates up to 100 sub-agents for complex tasks
• MoonViT-3D vision encoder handles native resolution images and video
• SWE-bench Verified 76.8% demonstrates real-world software engineering capability
• BrowseComp 78.4% in swarm mode shows practical multi-agent search value
• Modified MIT license allows self-hosting and commercial use
• 256K context window handles most long-document workloads

Cons:

• 1T parameters requires multi-node GPU infrastructure for self-hosting
• API pricing at $0.60/$3.00 per million tokens is premium-tier
• Modified MIT license has additional conditions versus standard MIT or Apache 2.0
• Agent Swarm adds latency and complexity for simple single-turn tasks
• Smaller third-party ecosystem compared to OpenAI or Google models
• No cache-hit pricing discount on the Moonshot API

Qwen3.5-35B-A3B: Pros and Cons

Pros:

• Runs on a single consumer GPU at 18-20 GB VRAM (FP8)
• Apache 2.0 license - the most permissive open-source license available
• Outperformed previous Qwen3-235B flagship despite being 7x smaller
• 262K context window (extendable to 1M+) matches or exceeds K2.5
• Zero marginal cost once hardware is provisioned
• Gated Delta Networks + MoE architecture achieves exceptional parameter efficiency
• Active community with growing ecosystem of fine-tunes and adapters

Cons:

• Raw benchmark scores trail K2.5 by 12-22 points on hard reasoning tasks
• No agent or multi-agent capabilities out of the box
• No built-in vision or multimodal support in this variant
• No official high-quality API from a major cloud provider
• 3B active parameters hit a ceiling on the hardest mathematical and coding problems
• Limited independent benchmarking on newer evaluation suites

Pricing Analysis

The economics are not even close if you are cost-sensitive. Qwen3.5-35B-A3B is free to run once you own the hardware, and the hardware it requires costs less than what most people spend on a gaming PC. K2.5's API at $0.60/$3.00 is not unreasonable for frontier quality, but it adds up quickly at scale. For guidance on running models locally, see our how to run open-source LLMs locally guide and the home GPU LLM leaderboard.

Verdict

Choose Kimi K2.5 if your workload demands the absolute best available reasoning, coding, or agentic capabilities. The Agent Swarm system, the MoonViT-3D vision encoder, and the benchmark scores all point to a model that belongs in the frontier tier alongside the best proprietary offerings. If you are working on complex multi-step research, mathematical problem-solving, or autonomous software engineering, K2.5 is worth every dollar of the API cost.

Choose Qwen3.5-35B-A3B if you need a model that deploys on commodity hardware and costs nothing to run. The performance-per-parameter ratio is extraordinary, and for the vast majority of production tasks - summarization, Q&A, code generation, content creation - it delivers results that would have been frontier-class 18 months ago. The Apache 2.0 license and single-GPU footprint make it the pragmatic choice for startups, solo developers, and teams that want full control of their inference stack.

The gap between these two models is real, but it is a gap measured in the hardest 10% of tasks. For the other 90%, Qwen3.5-35B-A3B running on your own GPU is hard to beat. For a broader view of where both models sit in the landscape, see our open-source LLM leaderboard and our guide to choosing an LLM in 2026.

Sources

• Kimi K2.5 - Moonshot AI
• Kimi K2.5 Model Card - Awesome Agents
• Qwen3.5-35B-A3B - Hugging Face
• Qwen3.5-35B-A3B Model Card - Awesome Agents
• Qwen3.5 Official Blog
• LLM Benchmarks 2026 - llm-stats.com
• Open Source LLM Leaderboard 2026 - VERTU