Model Comparison
GPT-5.6 Luna vs Jamba 1.5 LargeWhich is better in 2026?
GPT-5.6 Luna significantly outperforms across most benchmarks. GPT-5.6 Luna is 1.6x cheaper per token.
Verdict: GPT-5.6 Luna vs Jamba 1.5 Large — which is better?
GPT-5.6 Luna (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-5.6 Luna outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Large is better at 0 benchmarks. GPT-5.6 Luna significantly outperforms across most benchmarks.
On price, GPT-5.6 Luna is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.6 Luna also accepts a larger context window (1,050,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GPT-5.6 Luna if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 1.6x cheaper per token
- you process long inputs — it offers a 1,050,000 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Jamba 1.5 Large if…
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
GPT-5.6 Luna outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Large is better at 0 benchmarks.
GPT-5.6 Luna significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5.6 Luna ($1.00/1M tokens) is 2.0x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, GPT-5.6 Luna ($6.00/1M tokens) is 1.3x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than GPT-5.6 Luna.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5.6 Luna accepts 1,050,000 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 GPT-5.6 Luna is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
GPT-5.6 Luna supports multimodal inputs, whereas Jamba 1.5 Large does not.
GPT-5.6 Luna can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-5.6 Luna
Jamba 1.5 Large
License
Usage and distribution terms
GPT-5.6 Luna 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-5.6 Luna was released on 2026-07-09, while Jamba 1.5 Large was released on 2024-08-22.
GPT-5.6 Luna is 23 months newer than Jamba 1.5 Large.
Jul 9, 2026
1 weeks ago
1.9yr newerAug 22, 2024
1.9 years ago
Knowledge Cutoff
When training data ends
GPT-5.6 Luna has a knowledge cutoff of 2026-02-16, while Jamba 1.5 Large has a cutoff of 2024-03-05.
GPT-5.6 Luna has more recent training data (up to 2026-02-16), making it potentially better informed about events through that date compared to Jamba 1.5 Large (2024-03-05).
Feb 2026
1.9 yr newerMar 2024
Provider Availability
GPT-5.6 Luna is available from OpenAI. Jamba 1.5 Large is available from Bedrock, Google.
GPT-5.6 Luna
Jamba 1.5 Large
Outputs Comparison
Key Takeaways
GPT-5.6 Luna
View detailsOpenAI
Jamba 1.5 Large
View detailsAI21 Labs
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GPT-5.6 Luna and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GPT-5.6 Luna vs Jamba 1.5 Large.