Model Comparison
Jamba 1.5 Mini vs MiniMax M1 80KWhich is better in 2026?
MiniMax M1 80K significantly outperforms across most benchmarks. Jamba 1.5 Mini is 3.9x cheaper per token.
Verdict: Jamba 1.5 Mini vs MiniMax M1 80K — which is better?
Jamba 1.5 Mini (by AI21 Labs) and MiniMax M1 80K (by MiniMax) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Jamba 1.5 Mini outperforms in 0 benchmarks, while MiniMax M1 80K is better at 2 benchmarks (GPQA, MMLU-Pro). MiniMax M1 80K significantly outperforms across most benchmarks.
On price, Jamba 1.5 Mini is roughly 3.9x 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.
Choose Jamba 1.5 Mini if…
- cost matters — it's about 3.9x cheaper per token
Choose MiniMax M1 80K if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Jun 2025
Performance Benchmarks
Comparative analysis across standard metrics
Jamba 1.5 Mini outperforms in 0 benchmarks, while MiniMax M1 80K is better at 2 benchmarks (GPQA, MMLU-Pro).
MiniMax M1 80K significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Jamba 1.5 Mini ($0.20/1M tokens) is 2.8x cheaper than MiniMax M1 80K ($0.55/1M tokens).
For output processing, Jamba 1.5 Mini ($0.40/1M tokens) is 5.5x cheaper than MiniMax M1 80K ($2.20/1M tokens).
In conclusion, MiniMax M1 80K is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 404.0B more parameters than Jamba 1.5 Mini, making it 776.9% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 input tokens compared to Jamba 1.5 Mini's 256,144 tokens. Jamba 1.5 Mini can generate longer responses up to 256,144 tokens, while MiniMax M1 80K is limited to 40,000 tokens.
License
Usage and distribution terms
Jamba 1.5 Mini 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 Mini 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 Mini.
Aug 22, 2024
1.9 years ago
Jun 16, 2025
1.1 years ago
9mo newerKnowledge Cutoff
When training data ends
Jamba 1.5 Mini 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 Mini'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 Mini is available from Bedrock, Google. MiniMax M1 80K is available from Novita.
Jamba 1.5 Mini
MiniMax M1 80K
Outputs Comparison
Key Takeaways
Jamba 1.5 Mini
View detailsAI21 Labs
MiniMax M1 80K
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Jamba 1.5 Mini and MiniMax M1 80K side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Jamba 1.5 Mini vs MiniMax M1 80K.