Kimi K2-Thinking-0905 vs Particle-1
Kimi K2-Thinking-0905 and Particle-1 are closely matched at 35.4 and 31.9 on the LLM Stats Score. Particle-1 is 1.9x cheaper per token.
Moonshot AI · Particle AI · Updated for 2026
Which is better?
Kimi K2-Thinking-0905 and Particle-1 are closely matched on the overall LLM Stats Score at 35.4 and 31.9.
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 1.9x 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 Kimi K2-Thinking-0905
- you need open weights you can self-host or fine-tune
Choose Particle-1
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.9x 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
21 reported for Kimi K2-Thinking-0905 · 3 for Particle-1
Kimi K2-Thinking-0905 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, Kimi K2-Thinking-0905 ($0.47/1M tokens) is 1.9x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 2.0x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, Kimi K2-Thinking-0905 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 Kimi K2-Thinking-0905's 262,144 tokens. Kimi K2-Thinking-0905 can generate longer responses up to 262,144 tokens, while Particle-1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2-Thinking-0905
Particle-1
License
Usage and distribution terms
Kimi K2-Thinking-0905 is licensed under MIT, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K2-Thinking-0905 was released on 2025-09-05, while Particle-1 was released on 2026-10-28.
Particle-1 is 14 months newer than Kimi K2-Thinking-0905.
Sep 5, 2025
1.1 years ago
Oct 28, 2026
-3 days ago
1.1yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks. Particle-1 is available from Particle AI.
Kimi K2-Thinking-0905
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2-Thinking-0905 and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2-Thinking-0905 vs Particle-1.