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Models for India

A model for every need.

From compact models that run on a phone to frontier-grade reasoning with million-token context — curated, fine-tuned, and served for Indian languages and real-world workloads.

Text

20 models
vikasit-0.5b-writer
0.6B·32K ctx

Ultra-light writer. Good for text completion, simple Q&A, and edge devices.

vikasit-writer-0.8b
0.8B·32K ctx

Improved writer with refined architecture. Mobile and IoT friendly.

vikasit-nano
0.6B·32K ctx

Smallest general-purpose model. Autocomplete, quick responses, embedded use.

vikasit-mini
1.7B·128K ctx

Lightweight assistant. Summaries, chat, and basic reasoning.

vikasit-2b
2B·256K ctx

Edge-optimized. Multilingual, 256K context, on-device deployment.

vikasit-4b
4B·128K ctx

Balanced small model. Good code completion and multi-turn chat.

vikasit-3.5-4b
4B·128K ctx

Next-gen 4B with improved reasoning and multimodal awareness.

vikasit-8b
8B·128K ctx

Strong mid-range. Solid coding, analysis, and content generation.

vikasit-3-flashPopular
9B·128K ctx

Fast, capable model tuned for high-throughput, low-latency inference.

vikasit-14b
14B·256K ctx

Strong all-rounder. Complex reasoning, long documents, code review.

vikasit-27b
27B dense·256K ctx

Powerful dense model. Deep reasoning, advanced coding, research tasks.

vikasit-30b-moe
30B (3B active)·256K ctx

MoE efficiency — 30B quality at 3B inference cost. Fast and smart.

vikasit-32b
32B dense·256K ctx

Largest dense model on CPU. Best quality for reasoning and code.

vikasit-35b-moe
35B (3B active)·256K ctx

Latest MoE with architecture improvements. Best efficiency/quality ratio.

vikasit-3-coderPopular
80B (3B active)·262K ctx

Code-specialized MoE. FIM support, 262K context, agentic coding.

vikasit-120b
120B (5B active)·262K ctx

Datacenter MoE. Frontier reasoning at low inference cost — only ~5B active per token.

vikasit-235b-moe
235B (22B active)·262K ctx

Large MoE flagship. Advanced reasoning, agentic workflows, 262K context.

vikasit-reasoner-1t
1T (63B active)·262K ctx

Trillion-scale reasoning MoE. Deep multi-step reasoning, long-horizon agent tasks.

vikasit-titan-1t
1.1T (32B active)·262K ctx

Trillion-parameter agentic MoE. Native multimodal, 262K context, agent-swarm orchestration.

vikasit-titan-1.6tPopular
1.6T (49B active)·1M ctx

Flagship frontier MoE. 1.6T parameters, 1M-token context. Our most capable model.

Vision

3 models

Voice

3 models