DeepSeek-V4.1-Flash-1017 vs Jamba 1.5 Large
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 0.6. DeepSeek-V4.1-Flash-1017 is 6.7x cheaper per token.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 0.6, ranking #15 overall.
On price, DeepSeek-V4.1-Flash-1017 is roughly 6.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 6.7x 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 Oct 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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 8 for Jamba 1.5 Large
DeepSeek-V4.1-Flash-1017 and Jamba 1.5 Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 6.7x cheaper than Jamba 1.5 Large ($2.00/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 6.7x cheaper than Jamba 1.5 Large ($8.00/1M tokens).
In conclusion, Jamba 1.5 Large is more expensive than DeepSeek-V4.1-Flash-1017.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 365.2B more parameters than Jamba 1.5 Large, making it 91.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Jamba 1.5 Large's 256,000 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Jamba 1.5 Large does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
Jamba 1.5 Large
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 was released on 2026-10-17, while Jamba 1.5 Large was released on 2024-08-22.
DeepSeek-V4.1-Flash-1017 is 26 months newer than Jamba 1.5 Large.
Oct 17, 2026
0 days ago
2.2yr newerAug 22, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while DeepSeek-V4.1-Flash-1017'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-V4.1-Flash-1017's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Jamba 1.5 Large is available from Bedrock, Google.
DeepSeek-V4.1-Flash-1017
Jamba 1.5 Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Jamba 1.5 Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Jamba 1.5 Large.
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