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: 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: 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: 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: 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: computational biology tasks

How: Fine-tune ESM2 using NVIDIA BioNeMo recipes

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

Auto-discovered from news articles.

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: 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: 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

Auto-discovered from news articles.

Best for: governments, enterprises, and telcos

How: N/A

Example: N/A

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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: 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.

Auto-discovered from news articles.

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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