Model Comparison
GPT-4o mini vs Jamba 1.5 LargeWhich is better in 2026?
GPT-4o mini significantly outperforms across most benchmarks. GPT-4o mini is 13.3x cheaper per token.
Verdict: GPT-4o mini vs Jamba 1.5 Large — which is better?
GPT-4o mini (by OpenAI) and Jamba 1.5 Large (by AI21 Labs) 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.
GPT-4o mini outperforms in 2 benchmarks (GPQA, MMLU), while Jamba 1.5 Large is better at 0 benchmarks. GPT-4o mini significantly outperforms across most benchmarks.
On price, GPT-4o mini is roughly 13.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Large also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GPT-4o mini if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 13.3x cheaper per token
Choose Jamba 1.5 Large if…
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Aug 2024
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
GPT-4o mini outperforms in 2 benchmarks (GPQA, MMLU), while Jamba 1.5 Large is better at 0 benchmarks.
GPT-4o mini significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o mini ($0.15/1M tokens) is 13.3x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, GPT-4o mini ($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 GPT-4o mini.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Jamba 1.5 Large accepts 256,000 input tokens compared to GPT-4o mini's 128,000 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while GPT-4o mini is limited to 16,384 tokens.
Input Capabilities
Supported data types and modalities
GPT-4o mini supports multimodal inputs, whereas Jamba 1.5 Large does not.
GPT-4o mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4o mini
Jamba 1.5 Large
License
Usage and distribution terms
GPT-4o mini 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
GPT-4o mini was released on 2024-07-18, while Jamba 1.5 Large was released on 2024-08-22.
Jamba 1.5 Large is 1 month newer than GPT-4o mini.
Jul 18, 2024
2.0 years ago
Aug 22, 2024
1.9 years ago
1mo newerKnowledge Cutoff
When training data ends
GPT-4o mini has a knowledge cutoff of 2023-10-01, 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 GPT-4o mini (2023-10-01).
Oct 2023
Mar 2024
5 mo newerProvider Availability
GPT-4o mini is available from Azure. Jamba 1.5 Large is available from Bedrock, Google.
GPT-4o mini
Jamba 1.5 Large
Outputs Comparison
Key Takeaways
GPT-4o mini
View detailsOpenAI
Jamba 1.5 Large
View detailsAI21 Labs
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GPT-4o mini and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GPT-4o mini vs Jamba 1.5 Large.