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NVIDIA Nemotron 3 Super - blazing-fast agentic AI, ready to deploy today!

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All categories and models you can try out and directly use in deepinfra:

text-generation

automatic-speech-recognition

zero-shot-image-classification

featured

text-generation

NVIDIA-Nemotron-3-Super-120B-A12B

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B cover image

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

$0.04 cached, $0.10 in, $0.50 out / 1M

featured

text-generation

zai-org/GLM-5 cover image

GLM-5 is an advanced, open-source large language model designed for developers tackling the toughest challenges. It excels at long-context reasoning, multi-step tool orchestration, and complex systems engineering, making it the ideal choice for powering sophisticated agents and applications that require high-level cognitive tasks.

$0.16 cached, $0.80 in, $2.56 out / 1M

featured

Qwen/Qwen3-TTS cover image

Qwen3-TTS is an advanced text-to-speech model by Alibaba's Qwen team, delivering stable, expressive, and low-latency speech generation across 10 languages. Key capabilities: - 9 preset voices — Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee — covering diverse genders, ages, and accents - Voice cloning — clone any voice from a short (~3s) audio sample via the voice_id parameter - Instruction control — adjust tone, emotion, and speaking style with natural language (e.g. "speak slowly and calmly", "excited tone") - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese - Streaming support — real-time PCM streaming with ~97ms first-byte latency - Multiple output formats — WAV, MP3, FLAC, PCM Built on a 1.7B parameter architecture using discrete multi-codebook language modeling for end-to-end speech synthesis without cascading errors. Uses a custom 12Hz acoustic tokenizer that preserves paralinguistic information and environmental audio details.

$20.00 per 1M characters

featured

Qwen3-TTS-VoiceDesign

Qwen/Qwen3-TTS-VoiceDesign cover image

● Qwen3-TTS-VoiceDesign is a voice design variant of Qwen3-TTS by Alibaba's Qwen team. Instead of selecting from preset voices, you describe the voice you want in natural language — and the model generates speech in that voice. Key capabilities: - Natural language voice control — describe any voice with free text (e.g. "a deep male voice with a calm, authoritative presence", "a young cheerful female with a warm and friendly tone") - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese - Streaming support — real-time PCM streaming - Multiple output formats — WAV, MP3, FLAC, PCM Built on the same 1.7B parameter architecture as Qwen3-TTS, using discrete multi-codebook language modeling and a custom 12Hz acoustic tokenizer for high-quality end-to-end speech synthesis.

$20.00 per 1M characters

featured

text-generation

MiniMaxAI/MiniMax-M2.5 cover image

MiniMax M2.5 is SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks, boasting scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp (with context management).

$0.03 cached, $0.27 in, $0.95 out / 1M

featured

text-generation

Qwen/Qwen3-Max cover image

The latest flagship model in the Qwen family. State-of-the-art results across a comprehensive suite of benchmarks — including knowledge, reasoning, coding, instruction following, human preference alignment, agent tasks, and multilingual understanding.

$1.20 in $6.00 out $0.24 cached / 1M tokens

featured

text-generation

Qwen3-Max-Thinking

Qwen/Qwen3-Max-Thinking cover image

The latest flagship reasoning model in the Qwen3 family. Further enhanced by multiple innovations like adaptive tool-use and advanced test-time scaling techniques

$1.20 in $6.00 out $0.24 cached / 1M tokens

featured

text-generation

moonshotai/Kimi-K2.5 cover image

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

$0.07 cached, $0.45 in, $2.25 out / 1M

featured

text-generation

zai-org/GLM-4.7-Flash cover image

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

$0.01 cached, $0.06 in, $0.40 out / 1M

featured

text-generation

Nemotron-3-Nano-30B-A3B

nvidia/Nemotron-3-Nano-30B-A3B cover image

NVIDIA Nemotron 3 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compute cost while maintaining strong reasoning quality.

$0.05 in, $0.20 out / 1M

featured

text-generation

deepseek-ai/DeepSeek-V3.2 cover image

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

$0.13 cached, $0.26 in, $0.38 out / 1M

featured

chatterbox-turbo

ResembleAI/chatterbox-turbo cover image

Chatterbox is a family of three state-of-the-art, open-source text-to-speech models by Resemble AI. We are excited to introduce Chatterbox-Turbo, our most efficient model yet. Built on a streamlined 350M parameter architecture, Turbo delivers high-quality speech with less compute and VRAM than our previous models. We have also distilled the speech-token-to-mel decoder, previously a bottleneck, reducing generation from 10 steps to just one, while retaining high-fidelity audio output. Paralinguistic tags are now native to the Turbo model, allowing you to use [cough], [laugh], [chuckle], and more to add distinct realism. While Turbo was built primarily for low-latency voice agents, it excels at narration and creative workflows. If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (link).

$1.00 per 1M characters

featured

black-forest-labs/

FLUX-2-klein-4b

black-forest-labs/FLUX-2-klein-4b cover image

The fastest model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

$0.014 x (width / 1024) x (height / 1024)

featured

black-forest-labs/

FLUX-2-klein-9b

black-forest-labs/FLUX-2-klein-9b cover image

The best quality-to-latency ratio, production apps model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

$0.015 x (width / 1024) x (height / 1024)

bge-base-en-v1.5

BAAI/bge-base-en-v1.5 cover image

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

$0.005 / 1M tokens

BAAI/bge-en-icl cover image

A LLM-based embedding model with in-context learning capabilities that achieves SOTA performance on BEIR and AIR-Bench. It leverages few-shot examples to enhance task performance.

$0.010 / 1M tokens

bge-large-en-v1.5

BAAI/bge-large-en-v1.5 cover image

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

$0.010 / 1M tokens

BAAI/bge-m3 cover image

BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in over 100 languages and with input sizes up to 8192 tokens. The model can be used in a retrieval pipeline with hybrid retrieval and re-ranking to achieve higher accuracy and stronger generalization capabilities. BGE-M3 has shown state-of-the-art performance on several benchmarks, including MKQA, MLDR, and NarritiveQA, and can be used as a drop-in replacement for other embedding models like DPR and BGE-v1.5.

$0.010 / 1M tokens

BAAI/bge-m3-multi cover image

BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval), and Multi-Granularity (handling inputs from short queries to 8192-token documents). It achieves state-of-the-art retrieval performance across diverse benchmarks while maintaining a single model for multiple retrieval modes.

$0.010 / 1M tokens

Bria/Bria-3.2 cover image

Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to be on par to leading open-source models, and outperforming other licensed models.

Bria-3.2-vector

Bria/Bria-3.2-vector cover image

Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to be on par to leading open-source models, and outperforming other licensed models.

blur_background

Bria/blur_background cover image

Bria Blur Background softens and de-emphasizes image backgrounds while keeping the subject sharp and clear for professional-quality results. Trained fully on licensed data, it delivers safe, natural, and commercial-ready outputs.

Bria/enhance cover image

Bria Enhance improves overall image quality by sharpening details, balancing colors, and boosting clarity for crisp, professional visuals. Trained only on licensed data, it’s safe, reliable, and ready for commercial use.

Bria/erase cover image

Bria Eraser enables precise removal of unwanted objects from images while maintaining high-quality outputs. Trained exclusively on licensed data for safe and risk-free commercial use

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Latest Models

deepseek-ai/DeepSeek-V3.1 anthropic/claude-3-7-sonnet-latest deepseek-ai/DeepSeek-V3.2-Exp zai-org/GLM-4.6 moonshotai/Kimi-K2-Instruct-0905

Featured Models

Qwen/Qwen3-Max nvidia/Nemotron-3-Nano-30B-A3B black-forest-labs/FLUX-2-klein-4b moonshotai/Kimi-K2.5 zai-org/GLM-4.7-Flash

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