DeepSeek-V4-Pro-0813 vs Jamba 1.5 Large
Comparing DeepSeek-V4-Pro-0813 and Jamba 1.5 Large across benchmarks, pricing, and capabilities.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Jamba 1.5 Large trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4-Pro-0813 is roughly 6.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 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-V4-Pro-0813
- cost matters — it's about 6.4x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Jamba 1.5 Large
- you want predictable pricing at $2.00/M input and $8.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 ($0.43/1M tokens) is 4.6x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 9.2x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1202.0B more parameters than Jamba 1.5 Large, making it 302.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Jamba 1.5 Large's 256,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 was released on 2026-08-13, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-V4-Pro-0813 is 24 months newer than Jamba 1.5 Large.
Aug 13, 2026
1 weeks ago
2.0yr 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-V4-Pro-0813'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-V4-Pro-0813's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V4-Pro-0813
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Jamba 1.5 Large.