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DeepSeek-V4-Pro-Max vs LFM3-1.7B

DeepSeek-V4-Pro-Max leads the LLM Stats Score 42.6 to 25.0.

DeepSeek · Liquid AI · Updated for 2026

Which is better?

DeepSeek-V4-Pro-Max leads the overall LLM Stats Score 42.6 to 25.0, ranking #63 overall.

In the 5 individual benchmarks reported for both models, DeepSeek-V4-Pro-Max wins 5; this is a narrower head-to-head signal than the composite indexes.

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

Choose DeepSeek-V4-Pro-Max

  • overall performance matters — it scores 42.6 and ranks #63 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 5 of 5 exact shared results

Choose LFM3-1.7B

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

At a glance

The differences that matter most.

DeepSeek-V4-Pro-Max
DeepSeek
LFM3-1.7B
Liquid AI
Core performance indexes
LLM Stats Score
42.6
#63
25.0
#192
Reasoning
42.8
#56
20.8
#216
Coding
31.4
#62
7.4
#218
Agents
23.4
#80
18.1
#97
Cost, coverage & limits
Benchmark wins
5 of 5
0 of 5
Input price
$1.30 / M
— / M
Output price
$2.60 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4-Pro-Max
LFM3-1.7B
Math
40.2#15
7.6#285
Tool use
22.1#63
14.3#118
Long context
17.8#42
5.1#112
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

22 reported for DeepSeek-V4-Pro-Max · 20 for LFM3-1.7B

5 shared

DeepSeek-V4-Pro-Max outperforms in 5 benchmarks (GPQA, Humanity's Last Exam, MCP Atlas, SWE-bench Multilingual, SWE-Bench Verified), while LFM3-1.7B is better at 0 benchmarks.

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks.

Tue Nov 03 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

1598.3B diff

DeepSeek-V4-Pro-Max has 1598.3B more parameters than LFM3-1.7B, making it 92385.5% larger.

DeepSeek
DeepSeek-V4-Pro-Max
1.6Tparameters
Liquid AI
LFM3-1.7B
1.7Bparameters
1600.0B
DeepSeek-V4-Pro-Max
1.7B
LFM3-1.7B

Context Window

Maximum input and output token capacity

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

DeepSeek
DeepSeek-V4-Pro-Max
Input1,048,576 tokens
Output1,048,576 tokens
Liquid AI
LFM3-1.7B
Input- tokens
Output- tokens
Tue Nov 03 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V4-Pro-Max is licensed under MIT, while LFM3-1.7B 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-Max

MIT

Open weights

LFM3-1.7B

LFM Open License v1.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Pro-Max was released on 2026-04-23, while LFM3-1.7B was released on 2026-10-27.

LFM3-1.7B is 6 months newer than DeepSeek-V4-Pro-Max.

DeepSeek-V4-Pro-Max

Apr 23, 2026

6 months ago

LFM3-1.7B

Oct 27, 2026

5 days ago

6mo 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-V4-Pro-Max and LFM3-1.7B side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-Max
✓ Preferred
LFM3-1.7B
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-Max vs LFM3-1.7B.

Which is better, DeepSeek-V4-Pro-Max or LFM3-1.7B?

DeepSeek-V4-Pro-Max leads the LLM Stats Score 42.6 to 25.0. DeepSeek-V4-Pro-Max is made by DeepSeek and LFM3-1.7B 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-Pro-Max compare to LFM3-1.7B in benchmarks?

DeepSeek-V4-Pro-Max scores CodeForces: 100.0%, HMMT Feb 26: 95.2%, LiveCodeBench: 93.5%, MathArena Apex: 90.2%, GPQA: 90.1%. LFM3-1.7B scores IFEval: 96.7%, Multi-IF: 89.1%, LiveCodeBench v6: 73.5%, IFBench: 73.0%, BFCL-V4: 71.4%.

What are the context window sizes for DeepSeek-V4-Pro-Max and LFM3-1.7B?

DeepSeek-V4-Pro-Max supports 1.0M tokens and LFM3-1.7B 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-Max and LFM3-1.7B?

Key differences include LLM Stats Score (42.6 vs 25.0), licensing (MIT vs LFM Open License v1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Pro-Max and LFM3-1.7B?

DeepSeek-V4-Pro-Max is developed by DeepSeek and LFM3-1.7B is developed by Liquid AI.

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