Indic-native and multilingual models hosted on Indian infrastructure. All accessible via OpenAI-compatible API.
Instruction-tuned for 10 Indian languages. Strong for regional customer support and voice agent backends.
Flagship Indic chat model with extensive code-mixing support across 22 Indian languages.
High-throughput multilingual chat. Best economy option for English-first workloads with broad Indic coverage.
Google's latest multimodal model. Low-cost serving with native support for major Indian languages.
R1 chain-of-thought reasoning distilled from Qwen2.5-14B. Fits single A100 comfortably at FP16.
MoE design delivers frontier-quality reasoning at economy-tier throughput cost. 256K context window.
Strong coding and reasoning with hybrid thinking mode. High-context agentic and RAG workloads.
Best open reasoning model under 70B. Outperforms o1-mini on AIME 2024, MATH-500, and LiveCodeBench.
Highest-quality Indic model available. Dedicated GPU deployment — contact us to discuss fit.
Fast, accurate ASR across all 22 scheduled Indian languages including low-resource variants.
Whisper V3 fine-tuned on Vaani Hindi dataset. Handles diverse accents, noisy audio, and code-mixed speech.
State-of-the-art open ASR with unified offline and streaming inference. Language identification included.
Expressive TTS with 500+ speaker voices across 18 Indian languages. Prompt-controlled voice style.
44kHz TTS with zero-shot voice cloning and code-mixed text support across all 22 scheduled Indian languages.
MTEB-leading multilingual embeddings. Best for RAG over Indic and multilingual document corpora.
IBM's multilingual retrieval model with Matryoshka truncation and ONNX export. Minimal serving overhead.
Lightweight cross-encoder reranker for improving retrieval precision in multilingual RAG pipelines.
Encoder-only reranker with ~10x throughput advantage over decoder-based alternatives. Proven in production.
We evaluate new models continuously. Tell us what you need and we will check if it fits our serving infrastructure.
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