Particle-1 vs Phi-3.5-mini-instruct
Particle-1 leads the LLM Stats Score 31.9 to -4.3. Phi-3.5-mini-instruct is 4.4x cheaper per token.
Particle AI · Microsoft · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to -4.3, ranking #133 overall.
In the 2 individual benchmarks reported for both models, Particle-1 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 4.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Particle-1 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Particle-1
- overall performance matters — it scores 31.9 and ranks #133 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Phi-3.5-mini-instruct
- cost matters — it's about 4.4x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
3 reported for Particle-1 · 31 for Phi-3.5-mini-instruct
Particle-1 outperforms in 2 benchmarks (GPQA, MMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.
Particle-1 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Particle-1 ($0.25/1M tokens) is 2.5x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, Particle-1 ($1.00/1M tokens) is 10.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, Particle-1 is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Particle-1 accepts 1,048,576 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Particle-1 can generate longer responses up to 131,072 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Particle-1
Phi-3.5-mini-instruct
License
Usage and distribution terms
Particle-1 is licensed under a proprietary license, while Phi-3.5-mini-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Particle-1 was released on 2026-10-28, while Phi-3.5-mini-instruct was released on 2024-08-23.
Particle-1 is 27 months newer than Phi-3.5-mini-instruct.
Oct 28, 2026
-2 days ago
2.2yr newerAug 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.
Provider Availability
Particle-1 is available from Particle AI. Phi-3.5-mini-instruct is available from Azure.
Particle-1
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Particle-1 and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Particle-1 vs Phi-3.5-mini-instruct.