Jamba 1.5 Large vs MiniMax M3.1 Flash
MiniMax M3.1 Flash leads the LLM Stats Score 48.5 to 0.6. MiniMax M3.1 Flash is 13.3x cheaper per token.
AI21 Labs · MiniMax · Updated for 2026
Which is better?
MiniMax M3.1 Flash leads the overall LLM Stats Score 48.5 to 0.6, ranking #29 overall.
On price, MiniMax M3.1 Flash is roughly 13.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M3.1 Flash 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 MiniMax M3.1 Flash
- overall performance matters — it scores 48.5 and ranks #29 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 13.3x 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 MiniMax M3.1 Flash
Jamba 1.5 Large and MiniMax M3.1 Flashdon'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 13.3x more expensive than MiniMax M3.1 Flash ($0.15/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 13.3x more expensive than MiniMax M3.1 Flash ($0.60/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than MiniMax M3.1 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
MiniMax M3.1 Flash accepts 1,000,000 input tokens compared to Jamba 1.5 Large's 256,000 tokens. MiniMax M3.1 Flash can generate longer responses up to 524,288 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
MiniMax M3.1 Flash supports multimodal inputs, whereas Jamba 1.5 Large does not.
MiniMax M3.1 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
Jamba 1.5 Large
MiniMax M3.1 Flash
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while MiniMax M3.1 Flash 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 MiniMax M3.1 Flash was released on 2026-10-19.
MiniMax M3.1 Flash is 26 months newer than Jamba 1.5 Large.
Aug 22, 2024
2.2 years ago
Oct 19, 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 MiniMax M3.1 Flash'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 MiniMax M3.1 Flash's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. MiniMax M3.1 Flash is available from MiniMax.
Jamba 1.5 Large
MiniMax M3.1 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and MiniMax M3.1 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs MiniMax M3.1 Flash.