Jamba 1.5 Large vs Particle-1
Particle-1 leads the LLM Stats Score 31.9 to 0.5. Particle-1 is 8.0x cheaper per token.
AI21 Labs · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to 0.5, 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, Particle-1 is roughly 8.0x 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 Jamba 1.5 Large
- you need open weights you can self-host or fine-tune
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
- cost matters — it's about 8.0x 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
8 reported for Jamba 1.5 Large · 3 for Particle-1
Jamba 1.5 Large outperforms in 0 benchmarks, while Particle-1 is better at 2 benchmarks (GPQA, MMLU).
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, Jamba 1.5 Large ($2.00/1M tokens) is 8.0x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 8.0x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, Jamba 1.5 Large 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 Jamba 1.5 Large's 256,000 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while Particle-1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas Jamba 1.5 Large does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Large
Particle-1
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Jamba Open Model License
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Jamba 1.5 Large was released on 2024-08-22, while Particle-1 was released on 2026-10-28.
Particle-1 is 27 months newer than Jamba 1.5 Large.
Aug 22, 2024
2.2 years ago
Oct 28, 2026
1 days ago
2.2yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Particle-1's cutoff date is not specified.
We can confirm Jamba 1.5 Large's training data extends to 2024-03-05, but cannot make a direct comparison without Particle-1's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. Particle-1 is available from Particle AI.
Jamba 1.5 Large
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs Particle-1.