DeepSeek-R1-0528 vs Qwen3.5-9B
DeepSeek-R1-0528 and Qwen3.5-9B are closely matched at 24.1 and 24.7 on the LLM Stats Score. Qwen3.5-9B is 8.1x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-R1-0528 and Qwen3.5-9B are closely matched on the overall LLM Stats Score at 24.1 and 24.7.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Qwen3.5-9B is roughly 8.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.5-9B also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- you want predictable pricing at $0.50/M input and $2.15/M output
Choose Qwen3.5-9B
- your work emphasizes agents — it leads those capability indexes
- cost matters — it's about 8.1x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Mar 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 25 for Qwen3.5-9B
DeepSeek-R1-0528 outperforms in 2 benchmarks (MMLU-Pro, MMLU-Redux), while Qwen3.5-9B is better at 2 benchmarks (GPQA, HMMT 2025).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 5.0x more expensive than Qwen3.5-9B ($0.10/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 14.3x more expensive than Qwen3.5-9B ($0.15/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Qwen3.5-9B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 662.0B more parameters than Qwen3.5-9B, making it 7355.6% larger.
Context Window
Maximum input and output token capacity
Qwen3.5-9B accepts 262,144 input tokens compared to DeepSeek-R1-0528's 163,840 tokens. Qwen3.5-9B can generate longer responses up to 262,144 tokens, while DeepSeek-R1-0528 is limited to 163,840 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.5-9B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Qwen3.5-9B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Qwen3.5-9B
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Qwen3.5-9B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while Qwen3.5-9B was released on 2026-03-02.
Qwen3.5-9B is 9 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Mar 2, 2026
6 months ago
9mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Qwen3.5-9B is available from DeepInfra.
DeepSeek-R1-0528
Qwen3.5-9B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Qwen3.5-9B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Qwen3.5-9B.