Model Comparison
DeepSeek-V4-Flash-0731 vs Jamba 1.5 LargeWhich is better in 2026?
Comparing DeepSeek-V4-Flash-0731 and Jamba 1.5 Large across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V4-Flash-0731 vs Jamba 1.5 Large — which is better?
DeepSeek-V4-Flash-0731 (by DeepSeek) and Jamba 1.5 Large (by AI21 Labs) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, DeepSeek-V4-Flash-0731 is roughly 31.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-0731 if…
- cost matters — it's about 31.1x 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 Jul 2026
Choose Jamba 1.5 Large if…
- you want predictable pricing at $2.00/M input and $8.00/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and Jamba 1.5 Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 22.2x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 44.4x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Jamba 1.5 Large has 94.0B more parameters than DeepSeek-V4-Flash-0731, making it 30.9% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 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 DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 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-Flash-0731 was released on 2026-07-31, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-V4-Flash-0731 is 24 months newer than Jamba 1.5 Large.
Jul 31, 2026
3 days ago
1.9yr newerAug 22, 2024
1.9 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while DeepSeek-V4-Flash-0731'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-Flash-0731's cutoff date.
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Mar 2024
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Fireworks, Novita. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V4-Flash-0731
Jamba 1.5 Large
Outputs Comparison
Key Takeaways
Jamba 1.5 Large
View detailsAI21 Labs
No standout differentiators in the data we have for this pair.
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Jamba 1.5 Large.