DeepSeek-R1 vs Jamba 1.5 Mini
Comparing DeepSeek-R1 and Jamba 1.5 Mini across benchmarks, pricing, and capabilities.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-R1 and Jamba 1.5 Mini trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Jamba 1.5 Mini is roughly 3.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Jamba 1.5 Mini also accepts a larger context window (256,144 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 DeepSeek-R1
- you want the most recent training data — it shipped Jan 2025
Choose Jamba 1.5 Mini
- cost matters — it's about 3.8x cheaper per token
- you process long inputs — it offers a 256,144 token context window
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 8 for Jamba 1.5 Mini
DeepSeek-R1 and Jamba 1.5 Minidon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 2.8x more expensive than Jamba 1.5 Mini ($0.20/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 5.5x more expensive than Jamba 1.5 Mini ($0.40/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Jamba 1.5 Mini.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 619.0B more parameters than Jamba 1.5 Mini, making it 1190.4% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Mini accepts 256,144 input tokens compared to DeepSeek-R1's 131,072 tokens. Jamba 1.5 Mini can generate longer responses up to 256,144 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 Mini 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 Mini was released on 2024-08-22.
DeepSeek-R1 is 5 months newer than Jamba 1.5 Mini.
Jan 20, 2025
1.6 years ago
5mo newerAug 22, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Mini has a documented knowledge cutoff of 2024-03-05, while DeepSeek-R1's cutoff date is not specified.
We can confirm Jamba 1.5 Mini'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 Mini is available from Bedrock, Google.
DeepSeek-R1
Jamba 1.5 Mini
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Jamba 1.5 Mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Jamba 1.5 Mini.