Jamba 1.5 Mini vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to -5.7. Jamba 1.5 Mini is 4.1x cheaper per token.
AI21 Labs · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to -5.7, ranking #34 overall.
On price, Jamba 1.5 Mini is roughly 4.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 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 4.1x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 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 Mini · 18 for Mistral Large 4
Jamba 1.5 Mini and Mistral Large 4don'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 3.4x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, Jamba 1.5 Mini ($0.40/1M tokens) is 5.2x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 998.0B more parameters than Jamba 1.5 Mini, making it 1919.2% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Jamba 1.5 Mini's 256,144 tokens. Only Jamba 1.5 Mini specifies output context (256,144 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Jamba 1.5 Mini does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Mini
Mistral Large 4
License
Usage and distribution terms
Jamba 1.5 Mini is licensed under Jamba Open Model License, while Mistral Large 4 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 Mini was released on 2024-08-22, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 26 months newer than Jamba 1.5 Mini.
Aug 22, 2024
2.1 years ago
Oct 6, 2026
2 days ago
2.1yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Mini has a documented knowledge cutoff of 2024-03-05, while Mistral Large 4'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 Mistral Large 4's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Mini is available from Bedrock, Google. Mistral Large 4 is available from Mistral AI.
Jamba 1.5 Mini
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Mini and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Mini vs Mistral Large 4.