Jamba 1.5 Mini vs K-EXAONE-236B-A23B
K-EXAONE-236B-A23B leads the LLM Stats Score 26.2 to -5.7. Jamba 1.5 Mini is 2.8x cheaper per token.
AI21 Labs · LG AI Research · Updated for 2026
Which is better?
K-EXAONE-236B-A23B leads the overall LLM Stats Score 26.2 to -5.7, ranking #157 overall.
In the 1 individual benchmarks reported for both models, K-EXAONE-236B-A23B wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Jamba 1.5 Mini is roughly 2.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Mini also accepts a larger context window (256,144 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 Mini
- cost matters — it's about 2.8x cheaper per token
- you process long inputs — it offers a 256,144 token context window
- you need open weights you can self-host or fine-tune
Choose K-EXAONE-236B-A23B
- overall performance matters — it scores 26.2 and ranks #157 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 want the most recent training data — it shipped Dec 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 Mini · 6 for K-EXAONE-236B-A23B
Jamba 1.5 Mini outperforms in 0 benchmarks, while K-EXAONE-236B-A23B is better at 1 benchmark (MMLU-Pro).
K-EXAONE-236B-A23B 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 Mini ($0.20/1M tokens) is 3.0x cheaper than K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, Jamba 1.5 Mini ($0.40/1M tokens) is 2.5x cheaper than K-EXAONE-236B-A23B ($1.00/1M tokens).
In conclusion, K-EXAONE-236B-A23B is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
K-EXAONE-236B-A23B has 184.0B more parameters than Jamba 1.5 Mini, making it 353.8% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Mini accepts 256,144 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. Jamba 1.5 Mini can generate longer responses up to 256,144 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
License
Usage and distribution terms
Jamba 1.5 Mini is licensed under Jamba Open Model License, while K-EXAONE-236B-A23B 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 Mini was released on 2024-08-22, while K-EXAONE-236B-A23B was released on 2025-12-31.
K-EXAONE-236B-A23B is 17 months newer than Jamba 1.5 Mini.
Aug 22, 2024
2.1 years ago
Dec 31, 2025
8 months ago
1.4yr newerKnowledge Cutoff
When training data ends
Jamba 1.5 Mini has a knowledge cutoff of 2024-03-05, while K-EXAONE-236B-A23B has a cutoff of 2025-10-01.
K-EXAONE-236B-A23B has more recent training data (up to 2025-10-01), making it potentially better informed about events through that date compared to Jamba 1.5 Mini (2024-03-05).
Mar 2024
Oct 2025
1.6 yr newerProvider Availability
Jamba 1.5 Mini is available from Bedrock, Google. K-EXAONE-236B-A23B is available from FriendliAI.
Jamba 1.5 Mini
K-EXAONE-236B-A23B
Outputs Comparison
Judge for yourself.
Run your own prompts against Jamba 1.5 Mini and K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Jamba 1.5 Mini vs K-EXAONE-236B-A23B.