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DeepSeek-V4-Pro-0813 vs LFM2.5-VL-3B

Comparing DeepSeek-V4-Pro-0813 and LFM2.5-VL-3B across benchmarks, pricing, and capabilities.

DeepSeek · Liquid AI · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and LFM2.5-VL-3B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

  • you want the most recent training data — it shipped Aug 2026

Choose LFM2.5-VL-3B

  • you are already invested in the Liquid AI ecosystem

At a glance

The differences that matter most.

Benchmark wins
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
Released
Aug 2026
Aug 2026
License
MIT
LFM Open License v1.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 and LFM2.5-VL-3Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

1596.9B diff

DeepSeek-V4-Pro-0813 has 1596.9B more parameters than LFM2.5-VL-3B, making it 51124.9% larger.

DeepSeek
DeepSeek-V4-Pro-0813
1.6Tparameters
Liquid AI
LFM2.5-VL-3B
3.1Bparameters
1600.0B
DeepSeek-V4-Pro-0813
3.1B
LFM2.5-VL-3B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
Liquid AI
LFM2.5-VL-3B
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

LFM2.5-VL-3B supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.

LFM2.5-VL-3B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Pro-0813

Text
Images
Audio
Video

LFM2.5-VL-3B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 is licensed under MIT, while LFM2.5-VL-3B uses LFM Open License v1.0.

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

DeepSeek-V4-Pro-0813

MIT

Open weights

LFM2.5-VL-3B

LFM Open License v1.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while LFM2.5-VL-3B was released on 2026-08-12.

DeepSeek-V4-Pro-0813 is 0 month newer than LFM2.5-VL-3B.

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks ago

1d newer
LFM2.5-VL-3B

Aug 12, 2026

1 weeks 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-V4-Pro-0813 and LFM2.5-VL-3B side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
LFM2.5-VL-3B
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs LFM2.5-VL-3B.

Which is better, DeepSeek-V4-Pro-0813 or LFM2.5-VL-3B?

DeepSeek-V4-Pro-0813 (DeepSeek) and LFM2.5-VL-3B (Liquid AI) 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-V4-Pro-0813 compare to LFM2.5-VL-3B in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. LFM2.5-VL-3B scores DocVQA: 91.1%, POPE: 88.7%, RefCOCO-avg: 87.9%, TextVQA: 84.3%, OCRBench: 84.2%.

What are the context window sizes for DeepSeek-V4-Pro-0813 and LFM2.5-VL-3B?

DeepSeek-V4-Pro-0813 supports 1.0M tokens and LFM2.5-VL-3B 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-V4-Pro-0813 and LFM2.5-VL-3B?

Key differences include multimodal support (no vs yes), licensing (MIT vs LFM Open License v1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Pro-0813 and LFM2.5-VL-3B?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and LFM2.5-VL-3B is developed by Liquid AI.