Jamba 1.5 Large vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.4 to 0.9. Mistral Large 4 is 3.4x cheaper per token.
AI21 Labs · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.4 to 0.9, ranking #32 overall.
On price, Mistral Large 4 is roughly 3.4x 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 Large
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- overall performance matters — it scores 46.4 and ranks #32 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 3.4x cheaper per token
- 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 Large · 15 for Mistral Large 4
Jamba 1.5 Large 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 Large ($2.00/1M tokens) is 2.9x more expensive than Mistral Large 4 ($0.68/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 3.8x more expensive than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Mistral Large 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 652.0B more parameters than Jamba 1.5 Large, making it 163.8% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Jamba 1.5 Large's 256,000 tokens. Only Jamba 1.5 Large specifies output context (256,000 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Jamba 1.5 Large 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 Large
Mistral Large 4
License
Usage and distribution terms
Jamba 1.5 Large 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 Large 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 Large.
Aug 22, 2024
2.1 years ago
Oct 6, 2026
1 days ago
2.1yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large 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 Large'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 Large is available from Bedrock, Google. Mistral Large 4 is available from Mistral AI.
Jamba 1.5 Large
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs Mistral Large 4.