Compare / Qwen3.5 9B vs DeepSeek-R1-0528-Qwen3-8B

Qwen3.5 9B vs DeepSeek-R1-0528-Qwen3-8B

Updated November 2026

Verdict

Qwen3.5 9B scores higher on the Small LM Index: 63.9 vs 53.1. On the 2 benchmarks both labs report, Qwen3.5 9B wins 2 and DeepSeek-R1-0528-Qwen3-8B wins 0. Both need about 6.8 GB of memory at 4-bit quantization. Only Qwen3.5 9B has a hosted API on OpenRouter ($0.028 per million tokens); the other has to be self-hosted.

Pick Qwen3.5 9B if…

you want the strongest model overall
you mostly write code
you want a hosted API rather than running it yourself
you work with long documents
you need image input
you want the more widely used model (more quantized builds and community support)

Pick DeepSeek-R1-0528-Qwen3-8B if…

No clear advantage in the data we have.

Specifications

	Qwen3.5 9B	DeepSeek-R1-0528-Qwen3-8B
Small LM Index	63.9 (likely 58–69)	53.1 (likely 48–58)
Lab	Alibaba (Qwen) · China	DeepSeek · China
Released	2026-03-02	2025-05-29
Parameters	9B (dense)	8.2B (dense)
Memory at 4-bit	~6.4 GB	~6.8 GB
Context window	262K tokens	128K tokens
Input	text, image, video	text
Reasoning	Hybrid (thinking on/off)	Always thinks
License	Apache-2.0	MIT
API price / 1M tokens	$0.028 ($0.025 in, $0.036 out)	Self-host only
Hugging Face downloads (30 days)	6.4M	357.7K

Benchmarks

Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.

Benchmark	Qwen3.5 9B	DeepSeek-R1-0528-Qwen3-8B
MMLU-Pro Knowledge	82.5	≈ 74
GPQA Diamond Knowledge	81.7	61.1
AIME Math	≈ 88	76.3
LiveCodeBench Coding	65.6	60.5
τ²-Bench Agentic	79.1	—
IFBench Instruction following	64.5	—
MMMU-Pro Vision	69.2^✓	—

Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.

Questions

Which is better, Qwen3.5 9B or DeepSeek-R1-0528-Qwen3-8B?

Qwen3.5 9B scores higher on the Small LM Index: 63.9 vs 53.1. On the 2 benchmarks both labs report, Qwen3.5 9B wins 2 and DeepSeek-R1-0528-Qwen3-8B wins 0.

Which is cheaper to run?

Only Qwen3.5 9B has a hosted API on OpenRouter ($0.028 per million tokens); the other has to be self-hosted. Both need about 6.8 GB of memory at 4-bit quantization.

Which has the longer context window?

Qwen3.5 9B has the longer context window: 262K tokens against 128K.

Can I use Qwen3.5 9B and DeepSeek-R1-0528-Qwen3-8B commercially?

Qwen3.5 9B is released under Apache-2.0 and DeepSeek-R1-0528-Qwen3-8B under MIT. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.