GPT-5 nano vs Jamba 1.5 Large
GPT-5 nano leads the LLM Stats Score 19.5 to 0.9. GPT-5 nano is 25.5x cheaper per token.
OpenAI · AI21 Labs · Updated for 2026
Which is better?
GPT-5 nano leads the overall LLM Stats Score 19.5 to 0.9, ranking #206 overall.
In the 1 individual benchmarks reported for both models, GPT-5 nano wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-5 nano is roughly 25.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5 nano also accepts a larger context window (400,000 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-5 nano
- overall performance matters — it scores 19.5 and ranks #206 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 25.5x cheaper per token
- you process long inputs — it offers a 400,000 token context window
- you want the most recent training data — it shipped Aug 2025
Choose Jamba 1.5 Large
- 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
5 reported for GPT-5 nano · 8 for Jamba 1.5 Large
GPT-5 nano outperforms in 1 benchmarks (GPQA), while Jamba 1.5 Large is better at 0 benchmarks.
GPT-5 nano 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-5 nano ($0.05/1M tokens) is 40.0x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, GPT-5 nano ($0.40/1M tokens) is 20.0x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than GPT-5 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5 nano accepts 400,000 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 nano is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GPT-5 nano supports multimodal inputs, whereas Jamba 1.5 Large does not.
GPT-5 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-5 nano
Jamba 1.5 Large
License
Usage and distribution terms
GPT-5 nano 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 nano was released on 2025-08-07, while Jamba 1.5 Large was released on 2024-08-22.
GPT-5 nano is 12 months newer than Jamba 1.5 Large.
Aug 7, 2025
1.1 years ago
11mo newerAug 22, 2024
2.1 years ago
Knowledge Cutoff
When training data ends
GPT-5 nano has a knowledge cutoff of 2024-05-30, while Jamba 1.5 Large has a cutoff of 2024-03-05.
GPT-5 nano has more recent training data (up to 2024-05-30), making it potentially better informed about events through that date compared to Jamba 1.5 Large (2024-03-05).
May 2024
2 mo newerMar 2024
Provider Availability
GPT-5 nano is available from OpenAI. Jamba 1.5 Large is available from Bedrock, Google.
GPT-5 nano
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5 nano and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5 nano vs Jamba 1.5 Large.