Contamination Map Methodology

Text / LLM / Model

Qwen3.5

Organisation
Alibaba Cloud (Qwen Team)
Family
Qwen · version 3.5
Released
2026-02-16 (Hugging Face repo creation of Qwen/Qwen3.5-397B-A17B (API createdAt), https://huggingface.co/api/models/Qwen/Qwen3.5-397B-A17B; the Qwen3.6 card refers to 'the February release of the Qwen3.5 series')
Checkpoints
Qwen/Qwen3.5-397B-A17B, Qwen/Qwen3.5-122B-A10B, Qwen/Qwen3.5-35B-A3B, Qwen/Qwen3.5-35B-A3B-Base, Qwen/Qwen3.5-27B, Qwen/Qwen3.5-9B, Qwen/Qwen3.5-9B-Base, Qwen/Qwen3.5-4B, Qwen/Qwen3.5-4B-Base, Qwen/Qwen3.5-2B, Qwen/Qwen3.5-2B-Base, Qwen/Qwen3.5-0.8B, Qwen/Qwen3.5-0.8B-Base
Weights
Open · Apache 2.0
Links
Model card

Training data disclosure: Vague

Level determined for 0% of benchmark cells.

“Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3”
huggingface.co · model card · Qwen3.5 Highlights · retrieved 2026-10-15

Benchmark cells

Classic LLM benchmarks

Unknown MMLU (Measuring Massive Multitask Language Understanding) · training data not disclosed in enough detail

Unknown GSM8K (Grade School Math 8K) · training data not disclosed in enough detail

Unknown MATH (Mathematics Aptitude Test of Heuristics) · training data not disclosed in enough detail

Unknown HumanEval · training data not disclosed in enough detail

Unknown ARC-Challenge (AI2 Reasoning Challenge, Challenge Set) · training data not disclosed in enough detail

Unknown HellaSwag · training data not disclosed in enough detail

Unknown TruthfulQA · training data not disclosed in enough detail

Unknown WinoGrande · training data not disclosed in enough detail

Multimodal LLM benchmarks

Unknown MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning) · training data not disclosed in enough detail

Unknown MMMU-Pro · training data not disclosed in enough detail

Unknown MathVista · training data not disclosed in enough detail

Unknown ChartQA · training data not disclosed in enough detail

Unknown DocVQA · training data not disclosed in enough detail

Unknown AI2D (AI2 Diagrams) · training data not disclosed in enough detail

Unknown CharXiv · training data not disclosed in enough detail

Recent LLM benchmarks

Unknown GPQA Diamond · training data not disclosed in enough detail

Unknown MMLU-Pro · training data not disclosed in enough detail

Unknown LiveCodeBench (code generation, release_v6) · training data not disclosed in enough detail

Unknown SWE-bench Verified · training data not disclosed in enough detail

Unknown AIME 2024 · training data not disclosed in enough detail

Unknown AIME 2025 · training data not disclosed in enough detail

Unknown MATH-500 · training data not disclosed in enough detail

Notes

Natively multimodal (causal LM with vision encoder). No token count, data sources, cutoff or decontamination statement in the card; the blog (qwen.ai) could not be fetched as text. Accepts image input: “Type: Causal Language Model with Vision Encoder” Accepts image input: “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.”

Sources