DeepSeek-V3 vs Jamba 1.5 Large
DeepSeek-V3 leads the LLM Stats Score 15.8 to 1.2. DeepSeek-V3 is 7.3x cheaper per token.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-V3 leads the overall LLM Stats Score 15.8 to 1.2, ranking #211 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-V3 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3 is roughly 7.3x 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3
- overall performance matters — it scores 15.8 and ranks #211 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 7.3x cheaper per token
- you want the most recent training data — it shipped Dec 2024
Choose Jamba 1.5 Large
- you process long inputs — it offers a 256,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V3 · 8 for Jamba 1.5 Large
DeepSeek-V3 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks.
DeepSeek-V3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 7.4x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V3 ($1.10/1M tokens) is 7.3x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3 has 273.0B more parameters than Jamba 1.5 Large, 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-V3's 131,072 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while DeepSeek-V3 is limited to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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 + Model License (Commercial use allowed)
Open weights
Jamba Open Model License
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-V3 is 4 months newer than Jamba 1.5 Large.
Dec 25, 2024
1.7 years ago
4mo 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-V3'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's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V3 is available from DeepSeek. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V3
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 vs Jamba 1.5 Large.