AxionML Qwen3.5-35B-A3B-NVFP4
Developed by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.
This is an NVFP4-quantized version of Qwen/Qwen3.5-35B-A3B (35B (3B active) parameters), quantized using NVIDIA TensorRT Model Optimizer. Weights and activations of linear layers are quantized to FP4, reducing disk size and GPU memory by ~4x compared to BF16.
About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection strategies such as dual-pass evaluation comparing "map max to 6" versus "map max to 4 with clipping." On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity to reduce multiplier area while higher-precision FP32 accumulation protects dot-product accuracy.
Ready for commercial and non-commercial use under Apache 2.0.
For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Alibaba Cloud Model Studio.
In particular, Qwen3.5-Flash is the hosted version corresponding to Qwen3.5-35B-A3B with more production features, e.g., 1M context length by default and official built-in tools. For more information, please refer to the User Guide.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
Qwen3.5 Highlights
Qwen3.5 features the following enhancement:
Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.
For more details, please refer to our blog post Qwen3.5.
Model Overview
- Type: Causal Language Model with Vision Encoder
- Training Stage: Pre-training & Post-training
- Language Model
- Number of Parameters: 35B in total and 3B activated
- Hidden Dimension: 2048
- Token Embedding: 248320 (Padded)
- Number of Layers: 40
- Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
- Gated DeltaNet:
- Number of Linear Attention Heads: 32 for V and 16 for QK
- Head Dimension: 128
- Gated Attention:
- Number of Attention Heads: 16 for Q and 2 for KV
- Head Dimension: 256
- Rotary Position Embedding Dimension: 64
- Mixture Of Experts
- Number of Experts: 256
- Number of Activated Experts: 8 Routed + 1 Shared
- Expert Intermediate Dimension: 512
- LM Output: 248320 (Padded)
- MTP: trained with multi-steps
- Context Length: 262,144 natively and extensible up to 1,010,000 tokens.
Benchmark Results
Language
| Qwen3.5-35B-A3B | Qwen3.5-35B-A3B-NVFP4 | |
|---|---|---|
| Knowledge | ||
| MMLU-Pro | 85.3 | 84.0 |
| MMLU-Redux | 93.3 | 91.4 |
| C-Eval | 90.2 | 87.9 |
| SuperGPQA | 63.4 | 62.8 |
| Instruction Following | ||
| IFEval | 91.9 | 89.4 |
| IFBench | 70.2 | 68.4 |
| MultiChallenge | 60.0 | 58.9 |
| Long Context | ||
| AA-LCR | 58.5 | 57.0 |
| LongBench v2 | 59.0 | 58.2 |
| STEM & Reasoning | ||
| HLE w/ CoT | 22.4 | 22.0 |
| GPQA Diamond | 84.2 | 82.7 |
| HMMT Feb 25 | 89.0 | 87.4 |
| HMMT Nov 25 | 89.2 | 88.0 |
| Coding | ||
| SWE-bench Verified | 69.2 | 68.2 |
| Terminal Bench 2 | 40.5 | 39.8 |
| LiveCodeBench v6 | 74.6 | 73.5 |
| CodeForces | 2028 | 2002.4 |
| OJBench | 36.0 | 35.5 |
| FullStackBench en | 58.1 | 57.1 |
| FullStackBench zh | 55.0 | 54.2 |
| General Agent | ||
| BFCL-V4 | 67.3 | 66.1 |
| TAU2-Bench | 81.2 | 79.6 |
| VITA-Bench | 31.9 | 31.3 |
| DeepPlanning | 22.8 | 22.2 |
| Search Agent | ||
| HLE w/ tool | 47.4 | 45.7 |
| Browsecomp | 61.0 | 58.8 |
| Browsecomp-zh | 69.5 | 68.1 |
| WideSearch | 57.1 | 56.1 |
| Seal-0 | 41.4 | 40.5 |
| Multilingualism | ||
| MMMLU | 85.2 | 82.9 |
| MMLU-ProX | 81.0 | 78.0 |
| NOVA-63 | 57.1 | 55.0 |
| INCLUDE | 79.7 | 77.9 |
| Global PIQA | 86.6 | 85.5 |
| PolyMATH | 64.4 | 63.0 |
| WMT24++ | 76.3 | 74.0 |
| MAXIFE | 86.6 | 85.6 |
* CodeForces: evaluated on our own query set.
* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.
* Search Agent: most search agents built on our model adopt a simple context-folding strategy(256k): once the cumulative Tool Response length reaches a preset threshold, earlier Tool Responses are pruned from the history to keep the context within limits.
* WideSearch: we use a 256k context window without any context management.
* MMLU-ProX: we report the averaged accuracy on 29 languages.
* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
* Empty cells (--) indicate scores not yet available or not applicable.
Vision Language
| Qwen3.5-35B-A3B | Qwen3.5-35B-A3B-NVFP4 | |
|---|---|---|
| STEM and Puzzle | ||
| MMMU | 81.4 | 79.2 |
| MMMU-Pro | 75.1 | 73.1 |
| MathVision | 83.9 | 82.0 |
| Mathvista(mini) | 86.2 | 85.1 |
| DynaMath | 85.0 | 83.4 |
| ZEROBench | 8 | 7.8 |
| ZEROBench_sub | 34.1 | 33.2 |
| VlmsAreBlind | 97.0 | 95.4 |
| BabyVision | 38.4 / 29.6 | 38.4 / 29.6 |
| General VQA | ||
| RealWorldQA | 84.1 | 81.1 |
| MMStar | 81.9 | 79.4 |
| MMBenchEN-DEV-v1.1 | 91.5 | 90.1 |
| SimpleVQA | 58.3 | 56.9 |
| HallusionBench | 67.9 | 65.9 |
| Text Recognition and Document Understanding | ||
| OmniDocBench1.5 | 89.3 | 86.8 |
| CharXiv(RQ) | 77.5 | 75.2 |
| MMLongBench-Doc | 59.5 | 58.0 |
| CC-OCR | 80.7 | 78.3 |
| AI2D_TEST | 92.6 | 90.0 |
| OCRBench | 91.0 | 89.6 |
| Spatial Intelligence | ||
| ERQA | 64.8 | 63.8 |
| CountBench | 97.8 | 96.5 |
| RefCOCO(avg) | 89.2 | 87.2 |
| ODInW13 | 42.6 | 41.4 |
| EmbSpatialBench | 83.1 | 81.6 |
| RefSpatialBench | 63.5 | 62.5 |
| LingoQA | 79.2 | 77.6 |
| Hypersim | 13.1 | 12.6 |
| SUNRGBD | 33.4 | 33.0 |
| Nuscene | 14.6 | 14.3 |
| Video Understanding | ||
| VideoMME(w sub.) | 86.6 | 84.5 |
| VideoMME(w/o sub.) | 82.5 | 81.1 |
| VideoMMMU | 80.4 | 77.4 |
| MLVU | 85.6 | 84.2 |
| MVBench | 74.8 | 73.5 |
| LVBench | 71.4 | 69.9 |
| MMVU | 72.3 | 71.3 |
| Visual Agent | ||
| ScreenSpot Pro | 68.6 | 67.1 |
| OSWorld-Verified | 54.5 | 53.2 |
| AndroidWorld | 71.1 | 69.7 |
| Tool Calling | ||
| TIR-Bench | 55.5 / 38.0 | 55.5 / 38.0 |
| V* | 92.7 / 89.5 | 92.7 / 89.5 |
| Medical VQA | ||
| SLAKE | 78.7 | 77.3 |
| PMC-VQA | 62.0 | 60.9 |
| MedXpertQA-MM | 61.4 | 60.2 |
* MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.
* BabyVision: scores reported as "with CI / without CI".
* TIR-Bench and V*: scores reported as "with CI / without CI".
* Empty cells (--) indicate scores not yet available or not applicable.
Quantization Details
This model was quantized by applying NVFP4 to the weights and activations of linear operators within transformer blocks. The KV-cache is not quantized. Vision encoder weights are kept in their original precision.
- Quantization format: NVFP4 (MLP-only, MSE calibration)
- Calibration dataset: Nemotron-Post-Training-Dataset-v2
- Quantized checkpoint size: ~20GB
- Tool: TensorRT Model Optimizer
Usage
Deploy with SGLang
python3 -m sglang.launch_server \
--model-path AxionML/Qwen3.5-35B-A3B-NVFP4 \
--quantization modelopt_fp4 \
--tp 1 \
--reasoning-parser qwen3
Reproduce with ModelOpt
python3 examples/llm_ptq/hf_ptq.py \
--pyt_ckpt_path Qwen/Qwen3.5-35B-A3B \
--qformat nvfp4_mse \
--export_path ./qwen3.5-35b-a3b-nvfp4
Limitations
The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card for full details.
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