Model Comparison

DeepSeek-V3.1 vs DeepSeek VL2Which is better in 2026?

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

Verdict: DeepSeek-V3.1 vs DeepSeek VL2 — which is better?

DeepSeek-V3.1 (by DeepSeek) and DeepSeek VL2 (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek-V3.1 also accepts a larger context window (163,840 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek-V3.1 if…

  • you process long inputs — it offers a 163,840 token context window
  • you want the most recent training data — it shipped Jan 2025

Choose DeepSeek VL2 if…

  • you are already invested in the DeepSeek ecosystem
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.1 and DeepSeek VL2don'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

644.0B diff

DeepSeek-V3.1 has 644.0B more parameters than DeepSeek VL2, making it 2385.2% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
DeepSeek
DeepSeek VL2
27.0Bparameters
671.0B
DeepSeek-V3.1
27.0B
DeepSeek VL2

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to DeepSeek VL2's 129,280 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while DeepSeek VL2 is limited to 129,280 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Wed Jun 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

DeepSeek VL2 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

Text
Images
Audio
Video

License

Usage and distribution terms

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

deepseek

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek-V3.1

Jan 10, 2025

1.5 years ago

4w newer
DeepSeek VL2

Dec 13, 2024

1.5 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

Provider Availability

DeepSeek-V3.1 is available from DeepInfra, Novita. DeepSeek VL2 is available from Replicate.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

DeepSeek VL2

replicate logo
Replicate
* Prices shown are per million tokens

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

FAQ

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

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

DeepSeek-V3.1 (DeepSeek) and DeepSeek VL2 (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 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 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%.

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

DeepSeek-V3.1 supports 164K tokens and DeepSeek VL2 supports 129K 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?

Key differences include context window (164K vs 129K), multimodal support (no vs yes), licensing (MIT vs deepseek). See the full comparison above for benchmark-by-benchmark results.

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