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DeepSeek VL2 Small vs IBM Granite 4.2 30B

IBM Granite 4.2 30B leads the LLM Stats Score 24.0 to 1.5.

DeepSeek · IBM · Updated for 2026

Which is better?

IBM Granite 4.2 30B leads the overall LLM Stats Score 24.0 to 1.5, ranking #156 overall.

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

Choose DeepSeek VL2 Small

  • you are already invested in the DeepSeek ecosystem

Choose IBM Granite 4.2 30B

  • overall performance matters — it scores 24.0 and ranks #156 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Core performance indexes
1.5
#304
24.0
#156
-3.5
#322
21.8
#165
Cost, coverage & limits
Benchmark wins
Input price
— / M
— / M
Output price
— / M
— / M
Context window

Individual benchmarks

14 reported for DeepSeek VL2 Small · 18 for IBM Granite 4.2 30B

No common benchmarks found

DeepSeek VL2 Small and IBM Granite 4.2 30Bdon'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

14.0B diff

IBM Granite 4.2 30B has 14.0B more parameters than DeepSeek VL2 Small, making it 87.5% larger.

DeepSeek
DeepSeek VL2 Small
16.0Bparameters
IBM
IBM Granite 4.2 30B
30.0Bparameters
16.0B
DeepSeek VL2 Small
30.0B
IBM Granite 4.2 30B

Input capabilities

Documented input modalities across available providers

DeepSeek VL2 Small supports multimodal inputs, whereas IBM Granite 4.2 30B does not.

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

DeepSeek VL2 Small

Text
Images
Audio
Video

IBM Granite 4.2 30B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Small is licensed under deepseek, while IBM Granite 4.2 30B uses Apache 2.0.

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

DeepSeek VL2 Small

deepseek

Open weights

IBM Granite 4.2 30B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 Small was released on 2024-12-13, while IBM Granite 4.2 30B was released on 2026-08-25.

IBM Granite 4.2 30B is 21 months newer than DeepSeek VL2 Small.

DeepSeek VL2 Small

Dec 13, 2024

1.7 years ago

IBM Granite 4.2 30B

Aug 25, 2026

6 days ago

1.7yr newer

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 VL2 Small and IBM Granite 4.2 30B side-by-side, then vote on the output you prefer.

DeepSeek VL2 Small
✓ Preferred
IBM Granite 4.2 30B
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FAQ

Common questions about DeepSeek VL2 Small vs IBM Granite 4.2 30B.

Which is better, DeepSeek VL2 Small or IBM Granite 4.2 30B?

IBM Granite 4.2 30B leads the LLM Stats Score 24.0 to 1.5. DeepSeek VL2 Small is made by DeepSeek and IBM Granite 4.2 30B is made by IBM. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek VL2 Small compare to IBM Granite 4.2 30B in benchmarks?

DeepSeek VL2 Small scores DocVQA: 92.3%, ChartQA: 84.5%, OCRBench: 83.4%, TextVQA: 83.4%, MMBench: 80.3%. IBM Granite 4.2 30B scores RULER 64k: 90.0%, AIME 2025: 89.2%, HMMT25: 89.2%, RULER 128k: 81.4%, MMLU-Pro: 77.6%.

What are the main differences between DeepSeek VL2 Small and IBM Granite 4.2 30B?

Key differences include LLM Stats Score (1.5 vs 24.0), multimodal support (yes vs no), licensing (deepseek vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 Small and IBM Granite 4.2 30B?

DeepSeek VL2 Small is developed by DeepSeek and IBM Granite 4.2 30B is developed by IBM.