Model Comparison
GPT-4o vs Jamba 1.5 LargeWhich is better in 2026?
GPT-4o significantly outperforms across most benchmarks. Jamba 1.5 Large is 1.3x cheaper per token.
Verdict: GPT-4o vs Jamba 1.5 Large — which is better?
GPT-4o (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 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks. GPT-4o significantly outperforms across most benchmarks.
On price, Jamba 1.5 Large is roughly 1.3x 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 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
Choose Jamba 1.5 Large if…
- cost matters — it's about 1.3x cheaper per token
- 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 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks.
GPT-4o significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o ($2.50/1M tokens) is 1.3x more expensive than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, GPT-4o ($10.00/1M tokens) is 1.3x more expensive than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, GPT-4o is more expensive than Jamba 1.5 Large.*
* 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's 128,000 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while GPT-4o is limited to 16,384 tokens.
Input Capabilities
Supported data types and modalities
GPT-4o supports multimodal inputs, whereas Jamba 1.5 Large does not.
GPT-4o can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4o
Jamba 1.5 Large
License
Usage and distribution terms
GPT-4o 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 was released on 2024-08-06, while Jamba 1.5 Large was released on 2024-08-22.
Jamba 1.5 Large is 1 month newer than GPT-4o.
Aug 6, 2024
1.9 years ago
Aug 22, 2024
1.8 years ago
2w newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while GPT-4o's cutoff date is not specified.
We can confirm Jamba 1.5 Large's training data extends to 2024-03-05, but cannot make a direct comparison without GPT-4o's cutoff date.
—
Mar 2024
Provider Availability
GPT-4o is available from Azure, OpenAI. Jamba 1.5 Large is available from Bedrock, Google.
GPT-4o
Jamba 1.5 Large
Outputs Comparison
Key Takeaways
GPT-4o
View detailsOpenAI
Jamba 1.5 Large
View detailsAI21 Labs
Detailed Comparison
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FAQ
Common questions about GPT-4o vs Jamba 1.5 Large.