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DeepSeek-V4.1-Flash-1017 vs LFM2.5-2.6B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 18.4.

DeepSeek · Liquid AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 18.4, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 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-1017
DeepSeek
LFM2.5-2.6B
Liquid AI
Core performance indexes
LLM Stats Score
51.2
#15
18.4
#234
Reasoning
48.8
#18
14.1
#255
Coding
39.3
#14
16.0
#148
Agents
38.7
#8
6.7
#162
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
LFM2.5-2.6B
Tool use
31.3#6
0.3#197
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 9 for LFM2.5-2.6B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 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-1017 has 760.5B more parameters than LFM2.5-2.6B, making it 28271.9% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Liquid AI
LFM2.5-2.6B
2.7Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
2.7B
LFM2.5-2.6B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Liquid AI
LFM2.5-2.6B
Input- tokens
Output- tokens
Fri Oct 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas LFM2.5-2.6B does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

LFM2.5-2.6B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 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-1017

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-1017 was released on 2026-10-17, while LFM2.5-2.6B was released on 2026-08-04.

DeepSeek-V4.1-Flash-1017 is 2 months newer than LFM2.5-2.6B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

3 days ago

2mo newer
LFM2.5-2.6B

Aug 4, 2026

2 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?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and LFM2.5-2.6B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
LFM2.5-2.6B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs LFM2.5-2.6B.

Which is better, DeepSeek-V4.1-Flash-1017 or LFM2.5-2.6B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 18.4. DeepSeek-V4.1-Flash-1017 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-1017 compare to LFM2.5-2.6B in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. 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-1017 and LFM2.5-2.6B?

DeepSeek-V4.1-Flash-1017 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-1017 and LFM2.5-2.6B?

Key differences include LLM Stats Score (51.2 vs 18.4), 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-1017 and LFM2.5-2.6B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and LFM2.5-2.6B is developed by Liquid AI.

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