DeepSeek-R1 vs Jamba 1.5 Large
Comparing DeepSeek-R1 and Jamba 1.5 Large across benchmarks, pricing, and capabilities.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-R1 and Jamba 1.5 Large trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-R1 is roughly 3.6x 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-R1
- cost matters — it's about 3.6x cheaper per token
- you want the most recent training data — it shipped Jan 2025
Choose Jamba 1.5 Large
- you process long inputs — it offers a 256,000 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Jamba 1.5 Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 3.6x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 3.7x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-R1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 273.0B more parameters than Jamba 1.5 Large, making it 68.6% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Large accepts 256,000 input tokens compared to DeepSeek-R1's 131,072 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, 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.
MIT
Open weights
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-R1 is 5 months newer than Jamba 1.5 Large.
Jan 20, 2025
1.6 years ago
5mo newerAug 22, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while DeepSeek-R1'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 DeepSeek-R1's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-R1
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Jamba 1.5 Large.