The AI arena is free today

Open Superagent

LFM3-1.7B vs Phi-3.5-mini-instruct

LFM3-1.7B leads the LLM Stats Score 25.0 to -4.3.

Liquid AI · Microsoft · Updated for 2026

Which is better?

LFM3-1.7B leads the overall LLM Stats Score 25.0 to -4.3, ranking #192 overall.

In the 1 individual benchmarks reported for both models, LFM3-1.7B wins 1; 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 LFM3-1.7B

  • overall performance matters — it scores 25.0 and ranks #192 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Oct 2026

Choose Phi-3.5-mini-instruct

  • you want predictable pricing at $0.10/M input and $0.10/M output

At a glance

The differences that matter most.

LFM3-1.7B
Liquid AI
Phi-3.5-mini-instruct
Microsoft
Core performance indexes
LLM Stats Score
25.0
#192
-4.3
#387
Reasoning
20.8
#216
-5.2
#384
Coding
7.4
#218
-6.9
#297
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
— / M
$0.10 / M
Output price
— / M
$0.10 / M
Context window
—
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
LFM3-1.7B
Phi-3.5-mini-instruct
Math
7.6#285
-1.6#329
Long context
5.1#112
7.1#104
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for LFM3-1.7B · 31 for Phi-3.5-mini-instruct

1 shared

LFM3-1.7B outperforms in 1 benchmarks (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.

LFM3-1.7B significantly outperforms across most benchmarks.

Mon Nov 02 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

2.1B diff

Phi-3.5-mini-instruct has 2.1B more parameters than LFM3-1.7B, making it 119.7% larger.

Liquid AI
LFM3-1.7B
1.7Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
1.7B
LFM3-1.7B
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

Only Phi-3.5-mini-instruct specifies input context (128,000 tokens). Only Phi-3.5-mini-instruct specifies output context (128,000 tokens).

Liquid AI
LFM3-1.7B
Input- tokens
Output- tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Mon Nov 02 2026 • llm-stats.com

License

Usage and distribution terms

LFM3-1.7B is licensed under LFM Open License v1.0, while Phi-3.5-mini-instruct uses MIT.

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

LFM3-1.7B

LFM Open License v1.0

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

LFM3-1.7B was released on 2026-10-27, while Phi-3.5-mini-instruct was released on 2024-08-23.

LFM3-1.7B is 27 months newer than Phi-3.5-mini-instruct.

LFM3-1.7B

Oct 27, 2026

4 days ago

2.2yr newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.2 years 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 LFM3-1.7B and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.

LFM3-1.7B
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground

FAQ

Common questions about LFM3-1.7B vs Phi-3.5-mini-instruct.

Which is better, LFM3-1.7B or Phi-3.5-mini-instruct?

LFM3-1.7B leads the LLM Stats Score 25.0 to -4.3. LFM3-1.7B is made by Liquid AI and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does LFM3-1.7B compare to Phi-3.5-mini-instruct in benchmarks?

LFM3-1.7B scores IFEval: 96.7%, Multi-IF: 89.1%, LiveCodeBench v6: 73.5%, IFBench: 73.0%, BFCL-V4: 71.4%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

What are the context window sizes for LFM3-1.7B and Phi-3.5-mini-instruct?

LFM3-1.7B supports an unknown number of tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between LFM3-1.7B and Phi-3.5-mini-instruct?

Key differences include LLM Stats Score (25.0 vs -4.3), licensing (LFM Open License v1.0 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes LFM3-1.7B and Phi-3.5-mini-instruct?

LFM3-1.7B is developed by Liquid AI and Phi-3.5-mini-instruct is developed by Microsoft.

Related comparisons