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DeepSeek R1 Distill Llama 8B vs DeepSeek VL2 Tiny

DeepSeek R1 Distill Llama 8B leads the LLM Stats Score 7.0 to -4.7.

DeepSeek · DeepSeek · Updated for 2026

Which is better?

DeepSeek R1 Distill Llama 8B leads the overall LLM Stats Score 7.0 to -4.7, ranking #290 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek R1 Distill Llama 8B

  • overall performance matters — it scores 7.0 and ranks #290 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Jan 2025

Choose DeepSeek VL2 Tiny

  • you are already invested in the DeepSeek ecosystem

At a glance

The differences that matter most.

DeepSeek R1 Distill Llama 8B
DeepSeek
DeepSeek VL2 Tiny
DeepSeek
Core performance indexes
LLM Stats Score
7.0
#290
-4.7
#351
Reasoning
7.3
#282
-12.7
#362
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
—

Individual benchmarks

4 reported for DeepSeek R1 Distill Llama 8B · 14 for DeepSeek VL2 Tiny

No common benchmarks found

DeepSeek R1 Distill Llama 8B and DeepSeek VL2 Tinydon'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

Model Size

Parameter count comparison

5.0B diff

DeepSeek R1 Distill Llama 8B has 5.0B more parameters than DeepSeek VL2 Tiny, making it 167.7% larger.

DeepSeek
DeepSeek R1 Distill Llama 8B
8.0Bparameters
DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
8.0B
DeepSeek R1 Distill Llama 8B
3.0B
DeepSeek VL2 Tiny

Input capabilities

Documented input modalities across available providers

DeepSeek VL2 Tiny supports multimodal inputs, whereas DeepSeek R1 Distill Llama 8B does not.

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

DeepSeek R1 Distill Llama 8B

Text
Images
Audio
Video

DeepSeek VL2 Tiny

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Llama 8B 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 R1 Distill Llama 8B

MIT

Open weights

DeepSeek VL2 Tiny

deepseek

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Llama 8B was released on 2025-01-20, while DeepSeek VL2 Tiny was released on 2024-12-13.

DeepSeek R1 Distill Llama 8B is 1 month newer than DeepSeek VL2 Tiny.

DeepSeek R1 Distill Llama 8B

Jan 20, 2025

1.7 years ago

1mo newer
DeepSeek VL2 Tiny

Dec 13, 2024

1.8 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→

Judge for yourself.

Run your own prompts against DeepSeek R1 Distill Llama 8B and DeepSeek VL2 Tiny side-by-side, then vote on the output you prefer.

DeepSeek R1 Distill Llama 8B
✓ Preferred
DeepSeek VL2 Tiny
Open in Playground

FAQ

Common questions about DeepSeek R1 Distill Llama 8B vs DeepSeek VL2 Tiny.

Which is better, DeepSeek R1 Distill Llama 8B or DeepSeek VL2 Tiny?

DeepSeek R1 Distill Llama 8B leads the LLM Stats Score 7.0 to -4.7. DeepSeek R1 Distill Llama 8B is made by DeepSeek and DeepSeek VL2 Tiny is made by DeepSeek. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek R1 Distill Llama 8B compare to DeepSeek VL2 Tiny in benchmarks?

DeepSeek R1 Distill Llama 8B scores MATH-500: 89.1%, AIME 2024: 80.0%, GPQA: 49.0%, LiveCodeBench: 39.6%. DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%.

What are the main differences between DeepSeek R1 Distill Llama 8B and DeepSeek VL2 Tiny?

Key differences include LLM Stats Score (7.0 vs -4.7), multimodal support (no vs yes), licensing (MIT vs deepseek). See the full comparison above for benchmark-by-benchmark results.

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