AI Models

Best for: Most capable generally available model. Complex multi-step coding, long agentic workflows, 1M-token codebase reads.

How: client.messages.create(model='claude-opus-4-7', ...). Adaptive thinking is on by default — no separate extended-thinking mode needed.

Example: Use Claude Code CLI with --model claude-opus-4-7 to handle PR-sized refactors end-to-end in a single run.

API: api.anthropic.com (model: claude-opus-4-7) · AWS Bedrock · GCP Vertex AI · Microsoft Foundry

Step-change improvement in agentic coding vs Opus 4.6. New tokenizer means 1M tokens ≈ 555k words (vs 750k for Sonnet 4.6).

Best for: Cost-sensitive Chinese-market video, fast iteration on social shorts, longer narrative clips.

How: Two tiers — Seedance 2.0 Pro for quality and Seedance 2.0 Lite for fast/cheap drafts. Both expose text-to-video and image-to-video; v2 adds longer shot length and stronger prompt adherence over v1.

Example: POST volcengineapi.com/seedance/v2/videos { prompt: 'a hummingbird in flight, slow motion', mode: 'pro' }

API: Volcengine / ByteDance API

Successor to Seedance 1.0 (2025-06). ByteDance's competitor to Sora / Veo / Kling. The Lite tier remains notably cheaper than competitors at comparable quality.

Best for: Marketing reels, b-roll, storyboard previz, social-media shorts.

How: Generate up to 60s clips from a text prompt or seed image. Audio and lip-sync included.

Example: client.videos.generate(model='sora-2', prompt='aerial shot of a coastal city at sunrise, 1080p, 10s')

API: api.openai.com — client.videos.generate(model='sora-2')

Successor to Sora 1 — adds native audio and longer coherent shots.

Best for: Budget alternative to flagship models, Chinese language tasks

How: OpenAI SDK with base_url='https://api.moonshot.ai/v1'. WARNING: has implicit reasoning that eats max_tokens.

Example: Use moonshot-v1-8k instead for structured JSON tasks — kimi-k2.5 wastes tokens on hidden thinking.

API: api.moonshot.ai — OpenAI-compatible

Best for: Complex multi-step coding, large codebase refactors, long-document analysis

How: Best via Claude Code CLI for coding tasks. For API: messages.create() with system prompt + tools.

Example: claude-code: point it at a repo, describe the feature, it reads/edits/tests autonomously.

API: api.anthropic.com — SDK: pip install anthropic / npm i @anthropic-ai/sdk

Best for: Production API backends, real-time chat, moderate complexity coding

How: Drop-in replacement for Opus when you need faster/cheaper. Same API, just change model ID.

Example: Use as the default model in your API gateway — upgrade to Opus only for hard problems.

API: api.anthropic.com — same SDK as Opus

Best for: High-volume processing, real-time apps, budget-conscious pipelines

How: Set thinking_budget to control reasoning cost. 0 = no thinking, 24576 = max.

Example: Summarize 1000 GitHub issues per hour for a triage dashboard at ~$1.

API: Same SDK as Gemini Pro. model='gemini-2.5-flash-preview-05-20'

Best for: Photoreal cinematic clips, ad creative, talking-head shorts with audio.

How: Vertex AI: generate(model='veo-3.0-generate-preview', prompt='...'). Gemini API exposes the same model.

Example: ai.models.generate_videos(model='veo-3.0-generate-preview', prompt='timelapse of a city under heavy rain')

API: Vertex AI / Gemini API — model: veo-3.0-generate-preview

Best for: Pipelines, batch processing, structured data extraction, routing

How: Use for high-volume, low-complexity tasks: classification, extraction, summarization.

Example: Process 10K support tickets per hour to classify priority and extract entities.

API: api.anthropic.com — same SDK

Best for: General-purpose API integration, multimodal apps, coding assistance

How: client.chat.completions.create(model='gpt-4.1', messages=[...]). Supports vision, tools, JSON mode.

Example: Build a PR review bot that reads diffs + screenshots and posts comments.

API: api.openai.com — SDK: pip install openai / npm i openai

Best for: Embeddings preprocessing, log parsing, lightweight generation

How: Same API as GPT-4.1. Best for high-volume, simple tasks where cost matters.

Example: Parse 50K structured logs per hour and extract error patterns.

API: api.openai.com — same SDK

Best for: Intent classification, entity extraction at massive scale

How: Use for routing, tagging, simple extraction where quality bar is lower.

Example: Route 1M incoming messages per day to the right service for $4 total.

API: api.openai.com — same SDK

Best for: Hard math, science, multi-step planning, complex debugging

How: Use reasoning_effort param: 'low'/'medium'/'high'. No system prompt — use developer message instead.

Example: Debug a distributed system deadlock by feeding it the full trace + architecture.

API: api.openai.com — same SDK, just model='o3'

Best for: Coding with reasoning, moderate-complexity math, budget reasoning

How: Cheaper reasoning model. Use when o3 is overkill but you need chain-of-thought.

Example: Generate a migration plan for a database schema change with safety checks.

API: api.openai.com — same SDK

Best for: UI mockups, marketing assets, diagrams with text

How: Supports text overlays, inpainting, and style control. Best text rendering of any model.

Example: Generate architecture diagrams with accurate labels from a text description.

API: api.openai.com — client.images.generate(model='gpt-image-1')

Best for: Cost-sensitive video generation, dance / sports content.

How: Cheaper alternative to Sora/Veo with strong human-motion fidelity.

Example: POST klingai.com/v1/videos/text2video { prompt: '...', duration: 10 }

API: klingai.com — REST API

Best for: Long-document analysis, multimodal tasks, apps needing search grounding

How: client.models.generate_content(model='gemini-2.5-pro', contents=[...]). Supports grounding with Google Search.

Example: Feed a 200-page architecture doc and ask it to find security issues.

API: generativelanguage.googleapis.com — SDK: pip install google-genai

Best for: Short narrative content where the same character appears in multiple scenes.

How: Pass a reference image and a prompt; returns a 5–10s clip with consistent characters across shots.

Example: POST runwayml.com/v1/image_to_video { promptImage: ..., promptText: '...' }

API: runwayml.com — REST API + web app

Best for: Tasks needing real-time information, math-heavy problems

How: OpenAI SDK with base_url override. Also supports live search via tools.

Example: Monitor real-time tech news and generate summaries using live search.

API: api.x.ai — OpenAI-compatible SDK. Set base_url='https://api.x.ai/v1'

Best for: Budget reasoning tasks, math, lightweight chain-of-thought

How: Excellent cost-to-reasoning ratio. Use reasoning_effort param.

Example: Validate Terraform plans with reasoning about dependency chains for pennies.

API: api.x.ai — same as Grok 3

Best for: Social shorts, music videos, rapid creative iteration.

How: Strong for short, stylized clips and quick iteration. Pikaframes lets you set start/end frames.

Example: Use Pikaframes: upload start + end image, prompt the in-between motion.

API: pika.art — web app + API

Best for: High-quality image generation, product photography

How: API or self-host Flux.1 Schnell (open). Pro via API only.

Example: Generate product mockups for landing pages programmatically.

API: api.bfl.ml OR via Replicate, fal.ai

Best for: Batch processing, structured extraction, JSON pipelines

How: Best for structured output tasks. Supports response_format: json_object. No reasoning overhead.

Example: Process RSS feeds into structured summaries for pennies per 1000 articles.

API: api.moonshot.ai — OpenAI-compatible. model='moonshot-v1-8k'

Best for: RAG pipelines, semantic search, document retrieval

How: Set dimensions param to reduce size (e.g., 256 for fast search, 3072 for max quality).

Example: Index your internal docs and build a search API with pgvector + this model.

API: api.openai.com — client.embeddings.create(model='text-embedding-3-large')

Best for: computational biology tasks

How: Fine-tune ESM2 using NVIDIA BioNeMo recipes

Example: Fine-tuning ESM2 with LoRA for specific protein tasks

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Best for: Computational biology tasks

How: Use NVIDIA BioNeMo recipes for fine-tuning

Example: Fine-tuning ESM2 protein language models

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Best for: petite PC development

How: work with either Microsoft Windows or Linux

Example: use in AI development platforms

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Best for: use in infrastructure management tasks

How: connect AI to your infrastructure through the Model Context Protocol (MCP)

Example: AI assistants like GitHub Copilot, IBM Bob, Claude Code etc. to interact with Terraform through the Model Context Protocol (MCP)

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Best for: real-time AI applications such as chat assistants, copilots, and agentic workflows

How: Run DiffusionGemma on NVIDIA for high-throughput text generation

Example: Developers can leverage DiffusionGemma for building real-time AI applications

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Best for: Training speech AI models for clinical applications

How: Evaluate Clinical ASR Models Faster with Agent Skills and NVIDIA Nemotron Speech

Example: Training a speech AI model to correctly recognize drug names like Acetaminophen, Amlodipine

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Best for: maintaining context and completing tasks across many turns

How: deploy on Renesas RZ/V series for production

Example: use in chatbots evolving into long-running agents

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Best for: Maintaining context and efficiency across many turns

How: Integrate with existing chatbot frameworks to enhance long-running agent capabilities

Example: Use Nemotron 3 Ultra to power a chatbot that can reason and maintain context over multiple interactions

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Best for: Mobile and laptop applications requiring efficient AI models

How: Integrate Gemma 4 QAT models into your application for on-device AI processing

Example: Use Gemma 4 QAT for image recognition on smartphones with low latency and power consumption

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Best for: expanding on-device AI capabilities in Microsoft Edge

How: use Prompt and Writing Assistance APIs in Microsoft Edge

Example: integrated with Microsoft Edge for on-device AI tasks

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Best for: Advanced AI tasks

How: Integrate Mellum2 into your AI workflows

Example: Use Mellum2 for complex problem-solving and decision-making

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Best for: Developing Physical AI systems that need to understand and act within the real world

How: Integrate NVIDIA Cosmos 3 into your Physical AI system to enable reasoning and action capabilities

Example: Using NVIDIA Cosmos 3 to develop a robot that can understand and interact with its environment

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Best for: General AI applications

How: Integrate with Google I/O 2026

Example: Watch 9 videos showing the capabilities of Gemini 3.5

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Best for: General AI applications

How: Integrate with Google I/O 2026

Example: Watch 9 videos showing the capabilities of Gemini Omni

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Best for: financial trading landscape

How: Enables sophisticated analysis

Example: revolutionizing financial trading landscape

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Best for: conversational AI and content generation in Portuguese

How: Use ChatGPT API to integrate with applications

Example: Generate news articles in Portuguese

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Best for: governments, enterprises, and telcos

How: N/A

Example: N/A

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Best for: container workflow management

How: Integrate Gordon with Docker Desktop

Example: Gordon proposes fixes and takes action across your entire Docker workflow

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Best for: analyzing live code across critical parts of infrastructure

How: Point Mythos at live code to observe its strengths and weaknesses

Example: Mythos was used to analyze live code across critical parts of Cloudflare's infrastructure

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Best for: Agentic inference workloads

How: Integrate with NVIDIA's platform for inference

Example: Use for non-deterministic trajectories in AI

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Best for: AI-powered game development

How: Integrate NVIDIA DLSS 4.5 with Unreal Engine 5

Example: Game developers can enhance game performance and visuals

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Best for: Cost-sensitive Chinese-market video, fast iteration on social shorts.

How: Two tiers: Seedance 1.0 Pro for top quality and Seedance 1.0 Lite for fast/cheap drafts. Both expose text-to-video and image-to-video.

Example: POST volcengineapi.com/seedance/v1/videos { prompt: 'a hummingbird in flight, slow motion', mode: 'pro' }

API: Volcengine / ByteDance API

ByteDance's competitor to Sora / Veo / Kling. Lite tier is notably cheaper than competitors at similar quality.

Best for: multimodal agent reasoning in a single efficient open model

How: Run NVIDIA Nemotron 3 Nano Omni locally in a single command

Example: reasoning across screens, documents, audio, video, and text within a single perception-to-action loop

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Best for: enabling highly efficient operations

How: Build with DeepSeek V4 Using NVIDIA Blackwell and GPU-Accelerated Endpoints

Example: DeepSeek just launched its fourth generation of flagship models

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Best for: enabling highly efficient operations

How: Build with DeepSeek V4 Using NVIDIA Blackwell and GPU-Accelerated Endpoints

Example: DeepSeek just launched its fourth generation of flagship models

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Best for: powering AI applications

How: Deploy Google's 8th generation TPUs for your AI workloads

Example: Use the new TPUs for training and inference in AI applications

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Best for: AI acceleration

How: deploy Google TPUv8 in your cloud environment

Example: use Google TPUv8 for AI model training and inference

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Best for: AI acceleration

How: Deploy Google's 8th generation TPU for AI workloads.

Example: Use the TPU for training and inference of AI models.

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Best for: coding, research, and data analysis

How: Integrate GPT-5.5 into your tools for advanced tasks.

Example: Use GPT-5.5 for coding assistance or data analysis.

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Best for: AI applications requiring high-performance computing

How: deploy on Google Cloud to leverage the new TPU capabilities

Example: use for training and inference of large AI models

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Best for: text privacy and compliance

How: Integrate into text processing workflows

Example: Automatically redact sensitive information from documents

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Best for: AI acceleration

How: Deploy in Google Cloud for AI tasks

Example: Use for training and inference in AI applications

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Best for: enterprises to deploy and scale Codex

How: partner with Accenture, PwC, Infosys, and others

Example: help enterprises deploy and scale Codex across the software development lifecycle

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Best for: life sciences research

How: N/A

Example: N/A

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