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.
[!Tip]
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.
Benchmark Results
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
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.
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.
