Gemini 1.5 Flash vs Jamba 1.5 Large
Gemini 1.5 Flash and Jamba 1.5 Large are closely matched at 6.0 and 0.9 on the LLM Stats Score. Gemini 1.5 Flash is 13.3x cheaper per token.
Google · AI21 Labs · Updated for 2026
Which is better?
Gemini 1.5 Flash and Jamba 1.5 Large are closely matched on the overall LLM Stats Score at 6.0 and 0.9.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Gemini 1.5 Flash is roughly 13.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 1.5 Flash 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 Gemini 1.5 Flash
- cost matters — it's about 13.3x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose Jamba 1.5 Large
- you want the most recent training data — it shipped Aug 2024
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for Gemini 1.5 Flash · 8 for Jamba 1.5 Large
Gemini 1.5 Flash outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Jamba 1.5 Large is better at 2 benchmarks (GSM8k, MMLU).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Gemini 1.5 Flash ($0.15/1M tokens) is 13.3x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, Gemini 1.5 Flash ($0.60/1M tokens) is 13.3x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than Gemini 1.5 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 1.5 Flash accepts 1,048,576 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 Gemini 1.5 Flash is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Gemini 1.5 Flash supports multimodal inputs, whereas Jamba 1.5 Large does not.
Gemini 1.5 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemini 1.5 Flash
Jamba 1.5 Large
License
Usage and distribution terms
Gemini 1.5 Flash is licensed under a proprietary license, while Jamba 1.5 Large uses Jamba Open Model License.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
Gemini 1.5 Flash was released on 2024-05-01, while Jamba 1.5 Large was released on 2024-08-22.
Jamba 1.5 Large is 4 months newer than Gemini 1.5 Flash.
May 1, 2024
2.4 years ago
Aug 22, 2024
2.1 years ago
3mo newerKnowledge Cutoff
When training data ends
Gemini 1.5 Flash has a knowledge cutoff of 2023-11-01, while Jamba 1.5 Large has a cutoff of 2024-03-05.
Jamba 1.5 Large has more recent training data (up to 2024-03-05), making it potentially better informed about events through that date compared to Gemini 1.5 Flash (2023-11-01).
Nov 2023
Mar 2024
4 mo newerProvider Availability
Gemini 1.5 Flash is available from Google. Jamba 1.5 Large is available from Bedrock, Google.
Gemini 1.5 Flash
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemini 1.5 Flash and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemini 1.5 Flash vs Jamba 1.5 Large.