DeepSeek-V3.2-Speciale vs Sarvam-105B
DeepSeek-V3.2-Speciale leads the LLM Stats Score 34.3 to 25.9.
DeepSeek · Sarvam AI · Updated for 2026
Which is better?
DeepSeek-V3.2-Speciale leads the overall LLM Stats Score 34.3 to 25.9, ranking #87 overall.
In the 4 individual benchmarks reported for both models, DeepSeek-V3.2-Speciale wins 3; 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-V3.2-Speciale
- overall performance matters — it scores 34.3 and ranks #87 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 4 exact shared results
Choose Sarvam-105B
- you want the most recent training data — it shipped Mar 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for DeepSeek-V3.2-Speciale · 14 for Sarvam-105B
DeepSeek-V3.2-Speciale outperforms in 3 benchmarks (HMMT 2025, Humanity's Last Exam, SWE-Bench Verified), while Sarvam-105B is better at 1 benchmark (AIME 2025).
DeepSeek-V3.2-Speciale shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V3.2-Speciale has 580.0B more parameters than Sarvam-105B, making it 552.4% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2-Speciale specifies input context (131,072 tokens). Only DeepSeek-V3.2-Speciale specifies output context (131,072 tokens).
License
Usage and distribution terms
DeepSeek-V3.2-Speciale 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-V3.2-Speciale was released on 2025-12-01, while Sarvam-105B was released on 2026-03-06.
Sarvam-105B is 3 months newer than DeepSeek-V3.2-Speciale.
Dec 1, 2025
9 months ago
Mar 6, 2026
6 months ago
3mo newerKnowledge 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-V3.2-Speciale and Sarvam-105B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Speciale vs Sarvam-105B.