Qwen3.5-35B-A3B vs Nemotron 3 Nano 30B-A3B: Benchmarks vs Throughput in the 3B Active Parameter Class

Two models, nearly identical on paper - roughly 30B total parameters, roughly 3B active per token, both targeting single-GPU deployment, both open-weight. But Qwen3.5-35B-A3B and Nemotron 3 Nano 30B-A3B could not be more different under the hood. Qwen uses a Gated DeltaNet hybrid architecture with 256 experts. Nemotron uses a Mamba2-Transformer hybrid with 128 experts. One optimizes for benchmark accuracy. The other optimizes for tokens per second. The tradeoff between them is one of the most interesting engineering decisions in the open-weight model space right now.

Quick Comparison

Qwen3.5-35B-A3B: The Benchmark Machine

Qwen3.5-35B-A3B is the model that made the AI community do a double-take when it dropped on February 24, 2026. A 3B-active model posting MMLU-Pro 85.3 and GPQA Diamond 84.2 - scores that would have been flagship-tier a year ago - is not something anyone expected from this parameter range. It surpasses Alibaba's own Qwen3-235B-A22B across nearly every benchmark, which is a remarkable statement about how fast MoE architectures are improving.

The architecture is a hybrid of Gated DeltaNet layers and full softmax attention in a 3:1 ratio. Three out of every four layers use DeltaNet (a form of linear attention that scales O(n) with sequence length), while every fourth layer uses standard quadratic attention for global context. This means the model processes most of its context cheaply but still has access to the full-attention pattern where it matters. The MoE configuration is aggressive: 256 total experts with only 8 routed plus 1 shared active per token - an activation ratio of about 3.5%.

Where Qwen really separates itself is in agentic and vision benchmarks. TAU2-Bench at 81.2 is a 22.7-point improvement over the previous Qwen3-235B model on the same test. AndroidWorld at 71.1 and ScreenSpot Pro at 68.6 (compared to Claude Sonnet 4.5's 36.2 on the same benchmark) suggest this model understands GUI interaction at a level that most frontier models cannot match. It is natively multimodal - trained on text, image, and video data from scratch via early fusion - which means vision is not a bolted-on adapter but a core capability.

The model ships under Apache 2.0, the most developer-friendly license available. Quantized GGUF versions are available for consumer GPU deployment. For managed API access, Qwen3.5-Flash is the production-aligned hosted version at $0.10/M input tokens. For more on the broader Qwen 3.5 family, see the Qwen3.5-122B-A10B and Qwen3.5-27B model pages.

Nemotron 3 Nano 30B-A3B: The Throughput King

Nemotron 3 Nano arrived in December 2025 with a fundamentally different thesis: what if you optimized the architecture for inference speed first and let the benchmarks follow? NVIDIA's answer is a hybrid that interleaves 23 Mamba-2 layers, 23 MoE layers, and 6 grouped query attention layers across 52 total layers. The Mamba-2 layers process sequences in linear time - no quadratic attention costs - while the sparse attention layers (just 6 out of 52) provide the minimum global context needed for quality.

The throughput numbers back up the thesis. On a single H200 GPU with an 8K input / 16K output configuration, Nemotron 3 Nano achieves 3.3x higher throughput than Qwen3-30B-A3B on identical hardware. At 126 tokens per second on Artificial Analysis benchmarks, it is faster than the category average of 94 tok/s. For real-time applications - chatbots, coding assistants, agentic loops where you are making dozens of sequential LLM calls - this throughput advantage compounds into a massive latency reduction.

Long-context performance is the other strength worth noting. Nemotron scores 92.9% on RULER-100 at 256K tokens and 86.3% at the full 1M context window, significantly outperforming Qwen3-30B-A3B's 89.4% and 77.5% respectively on the same benchmark. The Mamba-2 layers with their linear complexity make long-context processing practically affordable. NVIDIA also built in a reasoning ON/OFF toggle with configurable thinking budgets - a practical feature for multi-agent systems where you want fast responses for routing decisions and deep reasoning for complex subtasks.

The model trained on 25 trillion tokens across 20 languages and 43 programming languages. Post-training used synchronous GRPO with Qwen3-Nemotron-235B as the reward model. Hardware support is broad - H100, A100, B200, RTX PRO 6000, Jetson Thor, DGX Spark - and NVIDIA provides FP8 quantized weights directly. The license is NVIDIA Open Model License, which is commercially permissive but not Apache 2.0 - worth reading the fine print if you are building a product around it.

Detailed Benchmark Comparison

The benchmark gap is stark on knowledge and coding tasks. Qwen leads by 7 points on MMLU-Pro, 11 points on GPQA, and a massive 30 points on SWE-bench Verified. The SWE-bench difference - 69.2 vs 38.8 - is the most meaningful gap here because it measures real-world code repair capability. A model that fixes 69% of real GitHub issues versus one that fixes 39% is not just "somewhat better" - it is a different capability tier for agentic coding workflows.

Nemotron fights back on throughput and long-context tasks. The 3.3x speed advantage is not a marketing number - it comes from architectural fundamentals (Mamba-2's linear complexity vs. attention's quadratic cost) and NVIDIA's inference stack optimization. The RULER scores at 1M tokens (86.3% vs Qwen's untested-at-1M performance) make Nemotron the clear pick for workloads that require reliable retrieval across massive context windows.

A note on benchmark methodology: both sets of numbers are self-reported by their respective creators. Qwen's benchmarks use their evaluation harness; Nemotron's use NVIDIA's NeMo Evaluator pipeline. Independent reproduction of these exact numbers by third parties is still pending for both models. See our Open Source LLM Leaderboard for community-verified scores as they become available.

Pricing and Deployment

For self-hosting, both models fit on a single high-end GPU in quantized form. Nemotron has the edge on framework support due to NVIDIA's TensorRT-LLM integration and official FP8 weights. Qwen's Gated DeltaNet is newer and framework support is still maturing in vLLM and SGLang - a practical concern if you are deploying to production today.

On API pricing, Nemotron's $0.06/M input through DeepInfra is cheaper, but Qwen3.5-Flash bundles a 1M context window and built-in tool calling at $0.10/M. The cost-per-useful-output calculation depends entirely on your task: if Qwen fixes 69% of coding issues in one pass versus Nemotron's 39%, the per-token price difference is irrelevant because you need fewer attempts. Conversely, if you are running a high-throughput chatbot where speed matters more than per-request accuracy, Nemotron's 3.3x throughput advantage translates directly to lower infrastructure costs.

The license difference matters for production. Apache 2.0 (Qwen) is the most permissive option - no strings attached. NVIDIA's Open Model License is commercially permissive but includes specific terms about model derivatives and redistribution that are worth reviewing with legal counsel. For context on the open-source licensing landscape, see our open source vs proprietary AI guide.

Pros and Cons

Qwen3.5-35B-A3B

Pros:

• MMLU-Pro 85.3 and GPQA 84.2 - highest in the 3B active parameter class by a wide margin
• SWE-bench 69.2 makes it viable for automated code repair workflows
• Native multimodal (text, image, video) - not just a text model with vision bolted on
• TAU2-Bench 81.2 and AndroidWorld 71.1 - best-in-class agentic benchmarks at this size
• Apache 2.0 license with 201 language support
• Part of the broader Qwen 3.5 family with managed API options

Cons:

• Gated DeltaNet framework support still maturing - deployment friction in production
• 256-expert MoE is sensitive to quantization quality
• Lower throughput than Mamba-based alternatives
• Self-reported benchmarks - independent verification pending
• Full 35B weights must stay in memory despite only 3B activating

Nemotron 3 Nano 30B-A3B

Pros:

• 3.3x throughput advantage over similarly-sized models on identical hardware
• 86.3% RULER at 1M tokens - best long-context retrieval in this class
• Configurable reasoning ON/OFF with token budgets for cost optimization
• Mature framework support via NVIDIA's TensorRT-LLM stack
• Official FP8 weights and broad hardware support (H100, A100, B200, RTX PRO, Jetson)

Cons:

• MMLU-Pro 78.3 trails Qwen by 7 points on broad knowledge
• SWE-bench 38.8 is less than half of Qwen's 69.2 - serious gap for coding tasks
• NVIDIA Open Model License is not Apache 2.0 - read the fine print
• Text-only model - no vision or multimodal capabilities
• Mamba-2 layer support still maturing in non-NVIDIA inference frameworks

Verdict

This is not a "one is better" comparison. These models make genuinely different architectural bets, and the right choice depends on what you are building.

Choose Qwen3.5-35B-A3B if your workload is benchmark-sensitive - coding agents, knowledge-intensive tasks, multimodal understanding, or anything where per-request accuracy matters more than throughput. The SWE-bench gap alone (69.2 vs 38.8) makes Qwen the only viable option for automated code repair in this weight class. The agentic benchmarks (TAU2-Bench 81.2) and native vision capabilities add further separation. Check the Coding Benchmarks Leaderboard to see where it ranks against the full field.

Choose Nemotron 3 Nano if your workload is throughput-sensitive - real-time chatbots, multi-agent systems with dozens of sequential LLM calls, or applications that require reliable performance across massive context windows (1M tokens). The 3.3x speed advantage is a genuine architectural moat, not a tuning optimization, and it compounds in latency-critical production environments. The configurable reasoning toggle is a practical tool for balancing cost and quality per-request.

Choose either if you need a 3B-active open-weight model for general-purpose tasks and are willing to trade benchmark headroom (Qwen) for speed (Nemotron) or vice versa. Both run on a single high-end GPU, both are commercially licensed, and both represent the state of the art for their respective architectural approaches. The honest engineering answer is that many production systems would benefit from running both - Qwen for accuracy-critical paths and Nemotron for latency-critical paths.

Sources:

• Qwen3.5-35B-A3B Model Card (HuggingFace)
• Qwen3.5: Towards Native Multimodal Agents (Blog)
• Nemotron 3 Nano Technical Report (NVIDIA Research)
• NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 Model Card (HuggingFace)
• Nemotron 3 Nano - Efficient, Open, Intelligent Models (HuggingFace Blog)
• NVIDIA NIM Model Card
• Nemotron 3 Nano Benchmarks and Pricing (llm-stats.com)
• Nemotron 3 Nano Technical Review (Medium)