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DeepSeek-R1 vs LFM3-VL-2B

Comparing DeepSeek-R1 and LFM3-VL-2B across benchmarks, pricing, and capabilities.

DeepSeek · Liquid AI · Updated for 2026

Which is better?

DeepSeek-R1 and LFM3-VL-2B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

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

Choose DeepSeek-R1

  • you want predictable pricing at $0.55/M input and $2.19/M output

Choose LFM3-VL-2B

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

At a glance

The differences that matter most.

DeepSeek-R1
DeepSeek
LFM3-VL-2B
Liquid AI
Benchmark wins
—
—
Input price
$0.55 / M
— / M
Output price
$2.19 / M
— / M
Context window
131,072
—

Individual benchmarks

0 reported for DeepSeek-R1 · 41 for LFM3-VL-2B

No common benchmarks found

DeepSeek-R1 and LFM3-VL-2Bdon'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

669.1B diff

DeepSeek-R1 has 669.1B more parameters than LFM3-VL-2B, making it 34847.9% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Liquid AI
LFM3-VL-2B
1.9Bparameters
671.0B
DeepSeek-R1
1.9B
LFM3-VL-2B

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Liquid AI
LFM3-VL-2B
Input- tokens
Output- tokens
Mon Nov 02 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

LFM3-VL-2B supports multimodal inputs, whereas DeepSeek-R1 does not.

LFM3-VL-2B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-R1

Text
Images
Audio
Video

LFM3-VL-2B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 is licensed under MIT, while LFM3-VL-2B uses LFM Open License v1.0.

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

DeepSeek-R1

MIT

Open weights

LFM3-VL-2B

LFM Open License v1.0

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while LFM3-VL-2B was released on 2026-10-27.

LFM3-VL-2B is 22 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.8 years ago

LFM3-VL-2B

Oct 27, 2026

4 days ago

1.8yr 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?

Judge for yourself.

Run your own prompts against DeepSeek-R1 and LFM3-VL-2B side-by-side, then vote on the output you prefer.

DeepSeek-R1
✓ Preferred
LFM3-VL-2B
Open in Playground

FAQ

Common questions about DeepSeek-R1 vs LFM3-VL-2B.

Which is better, DeepSeek-R1 or LFM3-VL-2B?

DeepSeek-R1 (DeepSeek) and LFM3-VL-2B (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-R1 compare to LFM3-VL-2B in benchmarks?

LFM3-VL-2B scores DocVQA: 96.7%, CountBench: 93.0%, ChartQA: 92.7%, RefCOCO-avg: 90.5%, POPE: 88.7%.

What are the context window sizes for DeepSeek-R1 and LFM3-VL-2B?

DeepSeek-R1 supports 131K tokens and LFM3-VL-2B 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-R1 and LFM3-VL-2B?

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-R1 and LFM3-VL-2B?

DeepSeek-R1 is developed by DeepSeek and LFM3-VL-2B is developed by Liquid AI.

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