DeepSeek-V4-Flash-0731 vs Jamba 1.5 Mini
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -5.7. DeepSeek-V4-Flash-0731 is 2.8x cheaper per token.
DeepSeek · AI21 Labs · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to -5.7, ranking #35 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 2.8x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- overall performance matters — it scores 44.7 and ranks #35 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 2.8x 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 Mini
- you want predictable pricing at $0.20/M input and $0.40/M output
At a glance
The differences that matter most.
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 8 for Jamba 1.5 Mini
DeepSeek-V4-Flash-0731 and Jamba 1.5 Minidon'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-Flash-0731 ($0.06/1M tokens) is 3.3x cheaper than Jamba 1.5 Mini ($0.20/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 2.2x cheaper than Jamba 1.5 Mini ($0.40/1M tokens).
In conclusion, Jamba 1.5 Mini is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 252.0B more parameters than Jamba 1.5 Mini, making it 484.6% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Jamba 1.5 Mini's 256,144 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while Jamba 1.5 Mini is limited to 256,144 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, 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.
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 Mini was released on 2024-08-22.
DeepSeek-V4-Flash-0731 is 24 months newer than Jamba 1.5 Mini.
Jul 31, 2026
1 months ago
1.9yr newerAug 22, 2024
2.1 years ago
Knowledge Cutoff
When training data ends
Jamba 1.5 Mini 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 Mini's training data extends to 2024-03-05, but cannot make a direct comparison without DeepSeek-V4-Flash-0731's cutoff date.
—
Mar 2024
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. Jamba 1.5 Mini is available from Bedrock, Google.
DeepSeek-V4-Flash-0731
Jamba 1.5 Mini
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Jamba 1.5 Mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Jamba 1.5 Mini.