DeepSeek-V2.5 vs Jamba 1.5 Large
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks. DeepSeek-V2.5 is 20.0x cheaper per token.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-V2.5 outperforms in 2 benchmarks (Arena Hard, GSM8k), while Jamba 1.5 Large is better at 1 benchmark (MMLU). DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
On price, DeepSeek-V2.5 is roughly 20.0x 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-V2.5
- you want the strongest raw capability — it leads on 2 of 3 shared benchmarks
- cost matters — it's about 20.0x cheaper per token
Choose Jamba 1.5 Large
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Aug 2024
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 2 benchmarks (Arena Hard, GSM8k), while Jamba 1.5 Large is better at 1 benchmark (MMLU).
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 14.3x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 28.6x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Jamba 1.5 Large has 162.0B more parameters than DeepSeek-V2.5, 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-V2.5's 8,192 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, 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.
deepseek
Open weights
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Jamba 1.5 Large was released on 2024-08-22.
Jamba 1.5 Large is 4 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Aug 22, 2024
2.0 years ago
3mo newerKnowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while DeepSeek-V2.5'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-V2.5's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V2.5
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Jamba 1.5 Large.