Jamba 1.5 Large vs MiniMax M1 80K
MiniMax M1 80K leads the LLM Stats Score 21.6 to 1.0. MiniMax M1 80K is 3.6x cheaper per token.
AI21 Labs · MiniMax · Updated for 2026
Which is better?
MiniMax M1 80K leads the overall LLM Stats Score 21.6 to 1.0, ranking #186 overall.
In the 2 individual benchmarks reported for both models, MiniMax M1 80K wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M1 80K is roughly 3.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M1 80K 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 want predictable pricing at $2.00/M input and $8.00/M output
Choose MiniMax M1 80K
- overall performance matters — it scores 21.6 and ranks #186 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 3.6x 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 Jun 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for Jamba 1.5 Large · 16 for MiniMax M1 80K
Jamba 1.5 Large outperforms in 0 benchmarks, while MiniMax M1 80K is better at 2 benchmarks (GPQA, MMLU-Pro).
MiniMax M1 80K 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 3.6x more expensive than MiniMax M1 80K ($0.55/1M tokens).
For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 3.6x more expensive than MiniMax M1 80K ($2.20/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than MiniMax M1 80K.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 58.0B more parameters than Jamba 1.5 Large, making it 14.6% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 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 MiniMax M1 80K is limited to 40,000 tokens.
License
Usage and distribution terms
Jamba 1.5 Large is licensed under Jamba Open Model License, while MiniMax M1 80K uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Jamba Open Model License
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Jamba 1.5 Large was released on 2024-08-22, while MiniMax M1 80K was released on 2025-06-16.
MiniMax M1 80K is 10 months newer than Jamba 1.5 Large.
Aug 22, 2024
2.0 years ago
Jun 16, 2025
1.2 years ago
9mo newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while MiniMax M1 80K'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 M1 80K's cutoff date.
Mar 2024
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Provider Availability
Jamba 1.5 Large is available from Bedrock, Google. MiniMax M1 80K is available from Novita.
Jamba 1.5 Large
MiniMax M1 80K
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Large and MiniMax M1 80K side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Large vs MiniMax M1 80K.