o4-mini vs Particle-1
Particle-1 leads the LLM Stats Score 31.9 to 26.8. Particle-1 is 4.4x cheaper per token.
OpenAI · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to 26.8, ranking #133 overall.
In the 1 individual benchmarks reported for both models, Particle-1 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Particle-1 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 o4-mini
- you want predictable pricing at $1.10/M input and $4.40/M output
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 1 of 1 exact shared results
- cost matters — it's about 4.4x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
14 reported for o4-mini · 3 for Particle-1
o4-mini outperforms in 0 benchmarks, while Particle-1 is better at 1 benchmark (GPQA).
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, o4-mini ($1.10/1M tokens) is 4.4x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, o4-mini ($4.40/1M tokens) is 4.4x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, o4-mini is more expensive than Particle-1.*
* 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 o4-mini's 200,000 tokens. Particle-1 can generate longer responses up to 131,072 tokens, while o4-mini is limited to 100,000 tokens.
Input capabilities
Documented input modalities across available providers
Both o4-mini and Particle-1 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
o4-mini
Particle-1
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
o4-mini was released on 2025-04-16, while Particle-1 was released on 2026-10-28.
Particle-1 is 19 months newer than o4-mini.
Apr 16, 2025
1.5 years ago
Oct 28, 2026
2 days ago
1.5yr newerKnowledge Cutoff
When training data ends
o4-mini has a documented knowledge cutoff of 2024-05-31, while Particle-1's cutoff date is not specified.
We can confirm o4-mini's training data extends to 2024-05-31, but cannot make a direct comparison without Particle-1's cutoff date.
May 2024
—
Provider Availability
o4-mini is available from OpenAI. Particle-1 is available from Particle AI.
o4-mini
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against o4-mini and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about o4-mini vs Particle-1.