Model Comparison

DeepSeek-V3.1 vs DeepSeek VL2 Tiny

Comparing DeepSeek-V3.1 and DeepSeek VL2 Tiny across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.1 and DeepSeek VL2 Tiny don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

668.0B diff

DeepSeek-V3.1 has 668.0B more parameters than DeepSeek VL2 Tiny, making it 22266.7% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
671.0B
DeepSeek-V3.1
3.0B
DeepSeek VL2 Tiny

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
DeepSeek
DeepSeek VL2 Tiny
Input- tokens
Output- tokens
Thu May 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

DeepSeek VL2 Tiny supports multimodal inputs, whereas DeepSeek-V3.1 does not.

DeepSeek VL2 Tiny can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

DeepSeek VL2 Tiny

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while DeepSeek VL2 Tiny uses deepseek.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.1

MIT

Open weights

DeepSeek VL2 Tiny

deepseek

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while DeepSeek VL2 Tiny was released on 2024-12-13.

DeepSeek-V3.1 is 1 month newer than DeepSeek VL2 Tiny.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

4w newer
DeepSeek VL2 Tiny

Dec 13, 2024

1.4 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
DeepSeek
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
DeepSeek
DeepSeek VL2 Tiny

FAQ

Common questions about DeepSeek-V3.1 vs DeepSeek VL2 Tiny.

Which is better, DeepSeek-V3.1 or DeepSeek VL2 Tiny?

DeepSeek-V3.1 (DeepSeek) and DeepSeek VL2 Tiny (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.1 compare to DeepSeek VL2 Tiny in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%.

What are the context window sizes for DeepSeek-V3.1 and DeepSeek VL2 Tiny?

DeepSeek-V3.1 supports 164K tokens and DeepSeek VL2 Tiny supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.1 and DeepSeek VL2 Tiny?

Key differences include multimodal support (no vs yes), licensing (MIT vs deepseek). See the full comparison above for benchmark-by-benchmark results.