GPT-4o vs Jamba 1.5 Large
GPT-4o leads the LLM Stats Score 14.3 to 1.1. Jamba 1.5 Large is 1.3x cheaper per token.
OpenAI · AI21 Labs · Updated for 2026
Which is better?
GPT-4o leads the overall LLM Stats Score 14.3 to 1.1, ranking #226 overall.
In the 3 individual benchmarks reported for both models, GPT-4o wins 3; this is a narrower head-to-head signal than the composite indexes.
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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-4o
- overall performance matters — it scores 14.3 and ranks #226 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
Choose Jamba 1.5 Large
- 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
38 reported for GPT-4o · 8 for Jamba 1.5 Large
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.
Human preference
Blind head-to-head votes and playground preference scores
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
Documented input modalities across available providers
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
2.1 years ago
Aug 22, 2024
2.0 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
Judge for yourself.
Run your own prompts against GPT-4o and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o vs Jamba 1.5 Large.