DeepSeek-R1 vs Qwen3.8 Flash
Comparing DeepSeek-R1 and Qwen3.8 Flash across benchmarks, pricing, and capabilities.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-R1 and Qwen3.8 Flash trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3.8 Flash is roughly 4.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.8 Flash also accepts a larger context window (1,000,000 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
- you need open weights you can self-host or fine-tune
Choose Qwen3.8 Flash
- cost matters — it's about 4.2x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 22 for Qwen3.8 Flash
DeepSeek-R1 and Qwen3.8 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 3.7x more expensive than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 4.7x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 546.0B more parameters than Qwen3.8 Flash, making it 436.8% larger.
Context Window
Maximum input and output token capacity
Qwen3.8 Flash accepts 1,000,000 input tokens compared to DeepSeek-R1's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Flash supports multimodal inputs, whereas DeepSeek-R1 does not.
Qwen3.8 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Qwen3.8 Flash
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Qwen3.8 Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 19 months newer than DeepSeek-R1.
Jan 20, 2025
1.6 years ago
Aug 26, 2026
4 days ago
1.6yr 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 is available from DeepSeek, DeepInfra, Together, Fireworks. Qwen3.8 Flash is available from Novita.
DeepSeek-R1
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Qwen3.8 Flash.