Qwen 3.5 Benchmark Results: How It Compares

Qwen 3.5 is a family of dense and Mixture-of-Experts (MoE) models from Alibaba Cloud, ranging from 9B to 397B parameters. This guide breaks down the official benchmark results and helps you understand where each model shines.

The Qwen 3.5 Lineup

Before diving into benchmarks, here is a quick overview of the models:

• Qwen3.5-9B — 9B dense, fastest open model
• Qwen3.5-27B — 27B dense, balanced performance
• Qwen3.5-35B-A3B — 35B MoE (3B active), efficient reasoning
• Qwen3.5-122B-A10B — 122B MoE (10B active), strong analysis
• Qwen3.5-397B-A17B — 397B MoE (17B active), flagship
• Qwen3.5-Flash — Hosted fast-path model
• Qwen3.5-Plus — Hosted premium model
• Qwen3.6-Plus — Latest hosted release with multimodal support

General Reasoning

On standard reasoning benchmarks (MMLU, MMLU-Pro, ARC-Challenge), the Qwen 3.5 family performs competitively:

• Qwen3.5-397B-A17B matches or exceeds GPT-4o on many reasoning tasks while using only 17B active parameters per forward pass.
• Qwen3.5-27B punches above its weight class, often competing with models 2–3x its size.
• Qwen3.5-9B delivers surprisingly strong results for its parameter count, especially on knowledge-based tasks.

The MoE architecture is a key advantage — the 397B model has the knowledge capacity of a much larger model but runs with the compute cost of a 17B model.

Coding Benchmarks

On HumanEval, MBPP, and LiveCodeBench:

• Qwen3.5-Plus and Qwen3.5-397B-A17B lead the family for code generation, approaching frontier model performance.
• Qwen3.5-35B-A3B is a sweet spot for coding tasks — it activates only 3B parameters but handles structured code output well.
• Qwen3.5-9B handles everyday coding tasks (boilerplate, simple functions, debugging) reliably.

For coding-specific workflows, enabling Thinking mode significantly improves multi-step code generation and debugging accuracy.

Math and Science

On GSM8K, MATH, and science reasoning benchmarks:

• The flagship 397B-A17B model excels at complex multi-step math problems.
• Qwen3.5-122B-A10B offers strong math performance with lower compute requirements.
• Thinking mode is especially impactful for math — it allows the model to show its reasoning chain, catching errors along the way.

Multilingual Performance

Qwen 3.5 has strong multilingual capabilities, particularly in Chinese and English:

• All models support Chinese and English natively with high quality.
• The larger models (122B, 397B, Plus) show competitive results across European and Asian languages.
• The 9B and 27B models are still capable for multilingual tasks but may lose nuance in lower-resource languages.

Context Window

All open Qwen 3.5 models support a 262K native context window, with extensibility up to approximately 1M tokens depending on the serving setup. The hosted models (Flash, Plus, Qwen3.6-Plus) offer 1M default context windows.

This makes Qwen 3.5 competitive with the longest-context models available, suitable for:

• Long document analysis
• Multi-turn conversations
• Code repository understanding
• Research paper summarization

How Qwen 3.5 Compares

Note: These comparisons are approximate and based on publicly available benchmark data. Performance varies by specific task and evaluation methodology.

Which Model Should You Choose?

• Quick tasks, low latency: Qwen3.5-9B or Qwen3.5-Flash
• Balanced everyday use: Qwen3.5-27B or Qwen3.5-Plus
• Complex reasoning: Qwen3.5-122B-A10B or Qwen3.5-397B-A17B
• Latest capabilities: Qwen3.6-Plus

Try It Yourself

Benchmarks tell part of the story, but the best way to evaluate is to try the models on your own tasks. Try Qwen 3.5 free — switch between models, enable thinking mode, and compare results side by side.