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DeepSeek-V4.1-Flash vs LFM2.5-2.6B

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 19.0.

DeepSeek · Liquid AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 19.0, ranking #13 overall.

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

Choose DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

Choose LFM2.5-2.6B

  • you are already invested in the Liquid AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
LFM2.5-2.6B
Liquid AI
Core performance indexes
LLM Stats Score
51.8
#13
19.0
#208
Reasoning
48.9
#18
14.7
#228
Coding
44.2
#5
17.4
#121
Agents
41.1
#4
7.9
#135
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash
LFM2.5-2.6B
Tool use
34.8#2
2.0#170
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 9 for LFM2.5-2.6B

No common benchmarks found

DeepSeek-V4.1-Flash and LFM2.5-2.6Bdon'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

760.5B diff

DeepSeek-V4.1-Flash has 760.5B more parameters than LFM2.5-2.6B, making it 28271.9% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Liquid AI
LFM2.5-2.6B
2.7Bparameters
763.2B
DeepSeek-V4.1-Flash
2.7B
LFM2.5-2.6B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Liquid AI
LFM2.5-2.6B
Input- tokens
Output- tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas LFM2.5-2.6B does not.

DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

LFM2.5-2.6B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while LFM2.5-2.6B uses LFM Open License v1.0.

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

DeepSeek-V4.1-Flash

MIT

Open weights

LFM2.5-2.6B

LFM Open License v1.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while LFM2.5-2.6B was released on 2026-08-04.

DeepSeek-V4.1-Flash is 1 month newer than LFM2.5-2.6B.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1mo newer
LFM2.5-2.6B

Aug 4, 2026

1 months 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-V4.1-Flash and LFM2.5-2.6B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
LFM2.5-2.6B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs LFM2.5-2.6B.

Which is better, DeepSeek-V4.1-Flash or LFM2.5-2.6B?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 19.0. DeepSeek-V4.1-Flash is made by DeepSeek and LFM2.5-2.6B is made by Liquid AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash compare to LFM2.5-2.6B in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. LFM2.5-2.6B scores Multi-IF: 80.1%, PinchBench: 68.2%, Claw-Eval: 62.8%, LiveCodeBench v6: 59.4%, IFBench: 59.2%.

What are the context window sizes for DeepSeek-V4.1-Flash and LFM2.5-2.6B?

DeepSeek-V4.1-Flash supports 1.0M tokens and LFM2.5-2.6B 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-V4.1-Flash and LFM2.5-2.6B?

Key differences include LLM Stats Score (51.8 vs 19.0), multimodal support (yes vs no), licensing (MIT vs LFM Open License v1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and LFM2.5-2.6B?

DeepSeek-V4.1-Flash is developed by DeepSeek and LFM2.5-2.6B is developed by Liquid AI.

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