DeepSeek-R1 vs Step3-VL-10B Comparison
Comparing DeepSeek-R1 and Step3-VL-10B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Step3-VL-10B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-R1 has 661.0B more parameters than Step3-VL-10B, making it 6610.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Step3-VL-10B supports multimodal inputs, whereas DeepSeek-R1 does not.
Step3-VL-10B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Step3-VL-10B
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Step3-VL-10B 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 was released on 2025-01-20, while Step3-VL-10B was released on 2026-01-15.
Step3-VL-10B is 12 months newer than DeepSeek-R1.
Jan 20, 2025
1.1 years ago
Jan 15, 2026
1 months ago
12mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
Step3-VL-10B
View detailsStepFun
Detailed Comparison
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