Model Comparison
DeepSeek-V3.2-Exp vs Jamba 1.5 LargeWhich is better in 2026?
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is 11.5x cheaper per token.
Verdict: DeepSeek-V3.2-Exp vs Jamba 1.5 Large — which is better?
DeepSeek-V3.2-Exp (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.
DeepSeek-V3.2-Exp outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks. DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2-Exp is roughly 11.5x 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.
Choose DeepSeek-V3.2-Exp if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 11.5x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Choose Jamba 1.5 Large if…
- you process long inputs — it offers a 256,000 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks.
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 7.4x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 19.5x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V3.2-Exp.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 287.0B more parameters than Jamba 1.5 Large, making it 72.1% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Large accepts 256,000 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V3.2-Exp 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-V3.2-Exp was released on 2025-09-29, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-V3.2-Exp is 13 months newer than Jamba 1.5 Large.
Sep 29, 2025
10 months ago
1.1yr 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-V3.2-Exp'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-V3.2-Exp's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V3.2-Exp is available from Novita. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V3.2-Exp
Jamba 1.5 Large
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
Jamba 1.5 Large
View detailsAI21 Labs
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2-Exp vs Jamba 1.5 Large.