Beam 1.0 vs Jamba 1.5 Mini
Beam 1.0 leads the LLM Stats Score 44.6 to -6.1. Jamba 1.5 Mini is 2.4x cheaper per token.
Reflection AI · AI21 Labs · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to -6.1, ranking #46 overall.
In the 1 individual benchmarks reported for both models, Beam 1.0 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Jamba 1.5 Mini is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Beam 1.0 also accepts a larger context window (1,048,576 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 Beam 1.0
- overall performance matters — it scores 44.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Jamba 1.5 Mini
- cost matters — it's about 2.4x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for Beam 1.0 · 8 for Jamba 1.5 Mini
Beam 1.0 outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Mini is better at 0 benchmarks.
Beam 1.0 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, Beam 1.0 ($0.40/1M tokens) is 2.0x more expensive than Jamba 1.5 Mini ($0.20/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 3.0x more expensive than Jamba 1.5 Mini ($0.40/1M tokens).
In conclusion, Beam 1.0 is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 449.3B more parameters than Jamba 1.5 Mini, making it 864.0% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 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 Beam 1.0 is limited to 131,072 tokens.
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Jamba 1.5 Mini uses Jamba Open Model License.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Jamba 1.5 Mini was released on 2024-08-22.
Beam 1.0 is 26 months newer than Jamba 1.5 Mini.
Oct 16, 2026
3 days ago
2.2yr newerAug 22, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a knowledge cutoff of 2026-06-30, while Jamba 1.5 Mini has a cutoff of 2024-03-05.
Beam 1.0 has more recent training data (up to 2026-06-30), making it potentially better informed about events through that date compared to Jamba 1.5 Mini (2024-03-05).
Jun 2026
2.3 yr newerMar 2024
Provider Availability
Beam 1.0 is available from Together, Reflection AI. Jamba 1.5 Mini is available from Bedrock, Google.
Beam 1.0
Jamba 1.5 Mini
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Jamba 1.5 Mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Jamba 1.5 Mini.