Jamba 1.5 Mini vs Pixtral Large
Pixtral Large leads the LLM Stats Score 11.8 to -5.5. Jamba 1.5 Mini is 12.0x cheaper per token.
AI21 Labs · Mistral AI · Updated for 2026
Which is better?
Pixtral Large leads the overall LLM Stats Score 11.8 to -5.5, ranking #245 overall.
On price, Jamba 1.5 Mini is roughly 12.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Mini also accepts a larger context window (256,144 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 Mini
- cost matters — it's about 12.0x cheaper per token
- you process long inputs — it offers a 256,144 token context window
Choose Pixtral Large
- overall performance matters — it scores 11.8 and ranks #245 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you want the most recent training data — it shipped Nov 2024
At a glance
The differences that matter most.
Individual benchmarks
8 reported for Jamba 1.5 Mini · 7 for Pixtral Large
Jamba 1.5 Mini and Pixtral Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Jamba 1.5 Mini ($0.20/1M tokens) is 10.0x cheaper than Pixtral Large ($2.00/1M tokens).
For output processing, Jamba 1.5 Mini ($0.40/1M tokens) is 15.0x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Pixtral Large has 72.0B more parameters than Jamba 1.5 Mini, making it 138.5% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Mini accepts 256,144 input tokens compared to Pixtral Large's 128,000 tokens. Jamba 1.5 Mini can generate longer responses up to 256,144 tokens, while Pixtral Large is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Pixtral Large supports multimodal inputs, whereas Jamba 1.5 Mini does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Mini
Pixtral Large
License
Usage and distribution terms
Jamba 1.5 Mini is licensed under Jamba Open Model License, while Pixtral Large uses Mistral Research License (MRL) for research; Mistral Commercial License for commercial use.
License differences may affect how you can use these models in commercial or open-source projects.
Jamba Open Model License
Open weights
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Release Timeline
When each model was launched
Jamba 1.5 Mini was released on 2024-08-22, while Pixtral Large was released on 2024-11-18.
Pixtral Large is 3 months newer than Jamba 1.5 Mini.
Aug 22, 2024
2.0 years ago
Nov 18, 2024
1.8 years ago
2mo newerKnowledge Cutoff
When training data ends
Jamba 1.5 Mini has a documented knowledge cutoff of 2024-03-05, while Pixtral Large's cutoff date is not specified.
We can confirm Jamba 1.5 Mini's training data extends to 2024-03-05, but cannot make a direct comparison without Pixtral Large's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Mini is available from Bedrock, Google. Pixtral Large is available from Mistral AI.
Jamba 1.5 Mini
Pixtral Large
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Mini and Pixtral Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Mini vs Pixtral Large.