Particle-1 vs Qwen3-235B-A22B-Thinking-2507
Particle-1 leads the LLM Stats Score 31.9 to 27.5. Particle-1 is 2.2x cheaper per token.
Particle AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to 27.5, 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 2.2x 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 1 of 1 exact shared results
- cost matters — it's about 2.2x 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
Choose Qwen3-235B-A22B-Thinking-2507
- 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 · 25 for Qwen3-235B-A22B-Thinking-2507
Particle-1 outperforms in 1 benchmarks (GPQA), while Qwen3-235B-A22B-Thinking-2507 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 1.2x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, Particle-1 ($1.00/1M tokens) is 3.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 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 Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Particle-1
Qwen3-235B-A22B-Thinking-2507
License
Usage and distribution terms
Particle-1 is licensed under a proprietary license, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Particle-1 was released on 2026-10-28, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
Particle-1 is 15 months newer than Qwen3-235B-A22B-Thinking-2507.
Oct 28, 2026
2 days ago
1.3yr newerJul 25, 2025
1.3 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. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
Particle-1
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Particle-1 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Particle-1 vs Qwen3-235B-A22B-Thinking-2507.