Model Comparison
DeepSeek-V2.5 vs Jamba 1.5 MiniWhich is better in 2026?
DeepSeek-V2.5 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 1.4x cheaper per token.
Verdict: DeepSeek-V2.5 vs Jamba 1.5 Mini — which is better?
DeepSeek-V2.5 (by DeepSeek) and Jamba 1.5 Mini (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-V2.5 outperforms in 3 benchmarks (Arena Hard, GSM8k, MMLU), while Jamba 1.5 Mini is better at 0 benchmarks. DeepSeek-V2.5 significantly outperforms across most benchmarks.
On price, DeepSeek-V2.5 is roughly 1.4x 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.
Choose DeepSeek-V2.5 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- cost matters — it's about 1.4x cheaper per token
Choose Jamba 1.5 Mini if…
- you process long inputs — it offers a 256,144 token context window
- you want the most recent training data — it shipped Aug 2024
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 3 benchmarks (Arena Hard, GSM8k, MMLU), while Jamba 1.5 Mini is better at 0 benchmarks.
DeepSeek-V2.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.4x cheaper than Jamba 1.5 Mini ($0.20/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x cheaper than Jamba 1.5 Mini ($0.40/1M tokens).
In conclusion, Jamba 1.5 Mini is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 184.0B more parameters than Jamba 1.5 Mini, making it 353.8% larger.
Context Window
Maximum input and output token capacity
Jamba 1.5 Mini accepts 256,144 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Jamba 1.5 Mini can generate longer responses up to 256,144 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 Mini 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 Mini was released on 2024-08-22.
Jamba 1.5 Mini is 4 months newer than DeepSeek-V2.5.
May 8, 2024
2.1 years ago
Aug 22, 2024
1.9 years ago
3mo newerKnowledge Cutoff
When training data ends
Jamba 1.5 Mini 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 Mini'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 Mini is available from Bedrock, Google.
DeepSeek-V2.5
Jamba 1.5 Mini
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
Jamba 1.5 Mini
View detailsAI21 Labs
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Jamba 1.5 Mini side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V2.5 vs Jamba 1.5 Mini.