DeepSeek-V4.1-Flash vs Sarvam-105B
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 25.7.
DeepSeek · Sarvam AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 25.7, ranking #12 overall.
In the 2 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you want the most recent training data — it shipped Sep 2026
Choose Sarvam-105B
- you are already invested in the Sarvam AI ecosystem
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-V4.1-Flash · 14 for Sarvam-105B
DeepSeek-V4.1-Flash outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while Sarvam-105B is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 658.2B more parameters than Sarvam-105B, making it 626.9% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Sarvam-105B does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Sarvam-105B
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Sarvam-105B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Sarvam-105B was released on 2026-03-06.
DeepSeek-V4.1-Flash is 6 months newer than Sarvam-105B.
Sep 10, 2026
0 days ago
6mo newerMar 6, 2026
6 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Sarvam-105B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Sarvam-105B.