The Technical Guide to Claude AI (2026): Models, Claude Code, and Enterprise Workflows

Introduction

Claude is no longer just a chatbot you prompt and paste from. In 2026, Anthropic has shipped a fully interconnected agentic ecosystem — one where the model lives in your terminal, reviews pull requests overnight, controls your mouse inside a spreadsheet, and coordinates teams of AI sub-agents working in parallel. The February 2026 release of Claude Sonnet 4.6 marks a decisive moment: Sonnet-class speed and pricing, Opus-class intelligence.

This guide is the unified technical manual that official docs, benchmark pages, and pricing tables don't give you. We'll cover:

• Claude Opus 4.6 vs. Sonnet 4.6 — which model, when, and why it matters for your stack

• Claude Code — the agentic CLI that's replaced "AI-assisted coding" with "AI-driven development"

• Enterprise integrations — Claude in Excel, PowerPoint, Chrome, and Cowork

• Pricing decoded — what Fast Mode and Batch API actually mean for your monthly bill

• Q&A covering the questions developers ask most

Let's build.

The Claude Model Family (2026 Update)

The Claude 4.6 family ships two models designed for different jobs. The decision between them isn't about intelligence anymore — it's about task architecture.

Claude Opus 4.6 — The Specialist

Opus 4.6 is the model you reach for when getting it exactly right is worth paying more and waiting longer. It's Anthropic's frontier reasoning engine, purpose-built for the highest-stakes agentic workloads.

Best for:

• Codebase-wide refactoring where a wrong assumption cascades across 50 files

• Multi-agent orchestration: Opus as the coordinator dispatching Sonnet sub-agents

• Deep research synthesis across very long documents

• Problems where nuance, ambiguity, and edge cases genuinely matter

Key capabilities:

• 1M token context window (beta) — enough to hold an entire production monorepo in a single request

• Adaptive Thinking — the model dynamically decides when to engage extended chain-of-thought reasoning versus fast inference. You don't set a thinking budget; the model calibrates to the complexity of the task

• Highest benchmark scores across complex reasoning evaluations, including DeepSearchQA and the most demanding agentic tasks

• Top performance on coordinating multi-agent workflows end-to-end

When to skip Opus: If the task is parallelizable, time-sensitive, or cost-sensitive at scale — Sonnet 4.6 now handles what previously required Opus 4.5.

Claude Sonnet 4.6 — The Workhorse (February 2026)

Sonnet 4.6 is Anthropic's most capable Sonnet model yet — a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design, with a 1M token context window in beta.

This is the model running as the default in claude.ai and Claude Cowork as of February 17, 2026. The headline claim is significant: performance that previously required an Opus-class model — including on real-world, economically valuable office tasks — is now available with Sonnet 4.6.

Best for:

• Everyday coding tasks, bug fixes, code review

• Computer use automation (form-filling, spreadsheet navigation, browser workflows)

• Long-document analysis at scale

• API workloads where cost-per-token matters at volume

What's new in 4.6 specifically:

Computer use reaches production viability. Early Sonnet 4.6 users are seeing human-level capability in tasks like navigating a complex spreadsheet or filling out a multi-step web form, before pulling it all together across multiple browser tabs. Sonnet 4.6 also shows major improvements in resisting prompt injection attacks during computer use — a critical security concern for any enterprise deployment.

Claude Code preference data tells the story. In Claude Code testing, users preferred Sonnet 4.6 over Sonnet 4.5 roughly 70% of the time, reporting that it more effectively read context before modifying code and consolidated shared logic rather than duplicating it. Users even preferred Sonnet 4.6 to Opus 4.5 — the November 2025 frontier model — 59% of the time.

Long-horizon planning. Sonnet 4.6's 1M token context window is enough to hold entire codebases, lengthy contracts, or dozens of research papers in a single request — and the model reasons effectively across all that context. In the Vending-Bench Arena evaluation, Sonnet 4.6 demonstrated emergent strategic behavior, investing heavily upfront then pivoting to profitability at precisely the right moment to outperform competing models.

Improved design sensibility. Customers independently described Sonnet 4.6's frontend outputs as more polished, with better layouts and animations — and reported fewer iteration rounds to reach production-quality results.

Pricing: Same as Sonnet 4.5 — $3 input / $15 output per million tokens.

Model Comparison Table

The practical read: Sonnet 4.6 has collapsed the use case gap with Opus. For most production workloads, start with Sonnet 4.6 and only escalate to Opus when you're running true deep-reasoning or orchestration jobs where correctness is non-negotiable.

Claude Code: The Agentic Terminal Interface

Claude Code is not a plugin. It's not a GitHub Copilot alternative. It's a full agentic coding environment that lives in your terminal — one that reads your codebase, understands your git history, spawns sub-agents, integrates with CI/CD pipelines, and runs tasks overnight while you sleep.

Think of it as a senior engineer who never needs to be onboarded because they read every file, every commit, and every comment before touching anything.

Installation & Setup

macOS / Linux (recommended):

curl -fsSL https://claude.ai/install.sh | sh

Windows:

winget install Anthropic.ClaudeCode

⚠️ Important: NPM installation (npm install -g @anthropic/claude-code) is deprecated as of 2026. Do not use it for new installations. Existing NPM installs should migrate to the native installer above to receive updates and new features.

Prerequisites:

• Node.js 18+ (runtime dependency, not the install method)

• Git 2.x or later

• A valid Anthropic API key or claude.ai Pro/Max/Team/Enterprise subscription

First run:

claude

Claude Code will prompt for authentication, scan your current directory, and initialize a local project context. It reads your .gitignore, project structure, and recent git log automatically.

Setting your default model:

claude config set model claude-sonnet-4-6
# or for maximum reasoning power:
claude config set model claude-opus-4-6

Key Capabilities

Agent Teams — parallel sub-agent execution

One of the most powerful features in the 2026 release is the ability for Claude Code to spawn multiple sub-agents working concurrently. You can instruct it to:

Review this PR across three dimensions in parallel: security, performance, and test coverage. 
Produce a unified report.

Internally, Claude Code orchestrates three agent threads, each focused on its domain, then synthesizes results into a single output. For large PRs or complex debugging sessions, this can dramatically reduce wall-clock time. Opus 4.6 is the recommended model for orchestration; Sonnet 4.6 is the recommended model for the sub-agents themselves.

Codebase awareness

Claude Code reads your full repository — not just the file you're editing. It understands:

• File dependency graphs (which modules import which)

• Git history ("this function was changed 3 times in the last month; here's why")

• Project conventions gleaned from existing code style

• Open issues or PR descriptions if you've connected GitHub via MCP

This context awareness is what separates it from autocomplete tools. When it refactors a function, it knows which callers to update.

GitHub Actions & GitLab CI/CD integration

Claude Code ships with native workflow templates for both platforms. You can configure it to run automatically on PR creation, performing tasks like:

• Test gap analysis ("which new code paths lack test coverage?")

• Dependency audit

• Auto-generating PR descriptions from diff content

• Suggesting fixes for failing CI checks

# .github/workflows/claude-review.yml
name: Claude Code Review
on: [pull_request]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: anthropic/claude-code-action@v2
        with:
          model: claude-sonnet-4-6
          task: review-pr
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}

Model Context Protocol (MCP)

MCP is the open standard Anthropic developed to let Claude securely connect to external data sources and tools — without those tools needing to be rebuilt as custom integrations.

When Claude Code has MCP connectors configured, it can pull context from your organization's full software ecosystem mid-task. Examples of what this unlocks:

• Jira: "Create a bug ticket for this failing test, linked to the relevant epic"

• Slack: "Summarize the last 48 hours of discussion in #backend-infra before I start this refactor"

• Google Drive: "Here's the product spec doc — implement the API described in Section 3"

• GitHub: Full PR and issue context beyond what's in the local repo

MCP connectors are configured once in ~/.claude/mcp.json and are automatically shared across Claude Code, the claude.ai web interface, and Claude in Excel. Set it up once; use it everywhere.

// ~/.claude/mcp.json
{
  "connectors": [
    { "type": "github", "token": "$GITHUB_TOKEN" },
    { "type": "jira", "host": "yourco.atlassian.net", "token": "$JIRA_TOKEN" },
    { "type": "slack", "token": "$SLACK_BOT_TOKEN" }
  ]
}

Claude Code Plugins: Extending Claude with Skills

Claude Code supports a plugin system built around the concept of Skills — folders of instructions, reference materials, and scripts that Claude loads dynamically at runtime to perform specialized tasks with consistent, high-quality output. Skills are how the community is extending Claude Code beyond its defaults, packaging domain expertise into shareable, installable modules.

What Is a Claude Code Plugin?

A plugin is a structured directory registered with Claude Code via a marketplace. When Claude Code encounters a task relevant to an installed skill, it reads that skill's SKILL.md file automatically — loading the distilled best practices for that task before writing a single line of code or generating a single document. The result is dramatically more consistent output without manual prompting every time.

Each skill follows the Agent Skills standard and contains at minimum:

• A SKILL.md file with YAML frontmatter (name, description) and Markdown instructions for Claude

• Optional bundled assets (fonts, scripts, templates, reference documentation)

LAXIMA Skills — Community Plugin Marketplace

One of the first public plugin marketplaces for Claude Code is LAXIMA Skills, maintained by LAXIMA Systems under an Apache 2.0 license. It ships six production-ready skills covering automation, design, frontend development, and internal communications.

Available skills:

Installation:

# Step 1: Register the LAXIMA marketplace
/plugin marketplace add laxima-tech/laxima-skills

# Step 2a: Install directly by name
/plugin install laxima-skills@laxima-skills

# Step 2b: Or browse interactively
# Run /plugin → Browse and install plugins → laxima-skills → Install now

Keeping skills up to date:

/plugin marketplace update

Repository structure:

laxima-skills/
├── .claude-plugin/
│   └── marketplace.json      # Marketplace registration metadata
├── skills/
│   ├── browser-testing/
│   │   └── SKILL.md
│   ├── canvas-design/
│   │   ├── SKILL.md
│   │   └── canvas-fonts/     # Bundled assets
│   ├── company-branding/
│   │   └── SKILL.md
│   ├── frontend-design/
│   │   └── SKILL.md
│   ├── skill-author/
│   │   └── SKILL.md
│   └── team-updates/
│       └── SKILL.md
├── template/
│   └── SKILL.md              # Starting point for creating your own skills
└── LICENSE

Building Your Own Skills

The plugin system is designed to be open. Any team can create a private or public skill marketplace. Using template/SKILL.md as a starting point, every skill needs:

1. A folder under skills/ with a SKILL.md file

2. YAML frontmatter with name and description fields

3. A Markdown body with structured instructions for Claude

For enterprise teams, this means you can encode your own coding standards, documentation templates, or compliance requirements into skills that Claude Code loads automatically — making every developer's output consistent with team conventions without requiring manual review cycles.

Prompt Engineering for Claude: Getting the Most Out of Every Request

Even with Claude Code automating complex workflows, there are countless tasks where the quality of your prompt is the direct determinant of output quality. Understanding how to structure prompts for Claude — and having access to a library of field-tested templates — significantly accelerates what you can accomplish.

What Makes a Claude Prompt Work

The most effective prompts for Claude share a consistent structure. They establish role and context (who Claude is acting as and what situation it's in), specify output format and length (table, numbered list, under 100 words, etc.), include concrete examples or constraints where relevant, and use bracketed placeholders for the parts that change between uses.

Claude is particularly responsive to:

• Explicit output structure requests: Asking for a specific number of sections, a table format, or bullet-point benefits vs. feature lists

• Negative constraints: "Do not add new facts" or "avoid technical jargon" shapes output as meaningfully as positive instructions

• Persona framing: Starting with "Act as a senior [role]" meaningfully shifts the model's approach and vocabulary

• Step decomposition: For complex tasks, specifying phases (Phase 1: assessment, Phase 2: implementation) mirrors how Claude Code's own agentic behavior is structured

The LAXIMA AI Prompt Library

For developers, data scientists, and technical teams who need copy-paste-ready prompts across the full range of business tasks, the LAXIMA AI Prompt Library provides 60 structured prompts organized across 10 categories. Every prompt is tested across Claude, ChatGPT, and Gemini, includes bracketed customization placeholders, and specifies output format explicitly.

Categories and highlights:

Development — Comprehensive repository analysis and bug-fixing prompt that walks through a seven-phase methodology: initial repository assessment, systematic bug discovery across five categories (critical, functional, integration, edge cases, code quality), documentation and prioritization, TDD-driven fix implementation, testing and validation, reporting, and continuous improvement recommendations.

AI System Prompts — Real, leaked system prompts from tools like Cursor IDE, Augment Code Agent, Atlassian Rovo Dev, Google NotebookLM, and Vercel V0.dev. Understanding how these tools prompt Claude and other models under the hood is invaluable for teams building their own AI-powered products.

Data & Analytics — SQL query generators with table schema context, A/B test result interpretation, KPI dashboard requirement specs, and monthly executive report narratives that transform raw metrics into stakeholder-ready communication.

Strategy & Planning — OKR generators, SWOT analyses, competitor brief structures, business model canvas prompts, and prioritization frameworks with impact-effort matrices.

Marketing & Content — App Store ASO metadata generation across 40+ locales with per-locale keyword strategy, ad copy A/B variant generators, blog post SEO outlines, and a social media series builder.

A practical example — the SQL generator prompt structure:

Write a SQL query for this request: "[describe what you need in plain language]". 
The database has these tables:
- [table1]: columns [col1, col2, ...]
- [table2]: columns [col1, col2, ...]

Include comments explaining each part. Use [PostgreSQL/MySQL/SQLite] syntax.

This pattern — natural language description, explicit schema context, output format specification, dialect selection — consistently produces commented, runnable SQL that doesn't require iteration. The same structural logic applies to every category in the library.

Prompts + Claude Code: The Combined Workflow

Prompts and skills serve different but complementary roles in the Claude ecosystem. A prompt is what you send when you need a specific, one-time output — a job description, a SQL query, a project status update. A skill is what Claude Code loads automatically when performing a class of tasks repeatedly — generating frontend components, writing team updates, running browser tests.

The practical workflow for technical teams: use the prompt library to accelerate one-off tasks and to prototype what good output looks like, then encode those patterns into Claude Code skills for tasks that recur across your team's workflow.

Enterprise Integrations: Claude in the Workflow

Claude in Excel

Claude in Excel has evolved from a "generate a formula" assistant into a genuine data analyst living inside the spreadsheet. The February 2026 update brings the most significant expansion yet.

What it can do:

• Interpret ambiguous, unstructured data in cells and columns ("clean this inconsistently formatted address list")

• Plan multi-step transformations ("normalize this financial model for Q1–Q4, then add a variance column against last year")

• Execute complex changes across large worksheets without manual formula authoring

• Produce analysis narrative inline alongside the data

MCP connectors in Excel (new in February 2026): The Claude in Excel add-in now supports MCP connectors, letting Claude work with external tools like S&P Global, LSEG, Daloopa, PitchBook, Moody's, and FactSet — pulling in external context without ever leaving Excel. If you've already set up MCP connectors in Claude.ai, those same connections work in Excel automatically. This is available on Pro, Max, Team, and Enterprise plans.

The practical implication: a financial analyst can ask Claude to pull current pricing data from an LSEG connector, populate a valuation model, run sensitivity analysis, and format the output — all from inside the spreadsheet, without switching context.

Claude in PowerPoint (Research Preview)

Claude in PowerPoint is currently in Research Preview. Its primary use case is generating presentation decks from structured data sources — Excel files, research documents, or raw Claude analysis output.

Current capabilities include:

• Slide structure generation from a data source or outline

• Chart and table insertion from Excel data

• Speaker note drafting

• Narrative flow suggestions across slide sequences

Given the Research Preview status, expect limitations in complex formatting and template fidelity. It's most useful today for generating a first-draft structure that humans then refine.

Cowork — The Desktop Agent

Cowork is Anthropic's desktop application layer designed for non-developer knowledge workers who need Claude to operate across local files, applications, and workflows without writing any code.

Key capabilities:

• Local file handling: Read, write, and reorganize files across your filesystem

• Cross-app workflows: Coordinate actions across applications running on your desktop

• Persistent task execution: Run longer tasks in the background without keeping a browser tab open

• Sonnet 4.6 default: Claude Sonnet 4.6 is now the default model in Claude Cowork, bringing the full upgrade in computer use and reasoning to desktop workflows

Cowork targets the team member who isn't writing Python scripts but does need to process hundreds of documents, manage structured data across tools, or run repeatable multi-step workflows autonomously.

Claude in Chrome

Claude in Chrome is a browsing agent that runs Claude directly in your browser, enabling it to:

• Read and summarize content on any page

• Fill forms and navigate multi-step web workflows autonomously

• Conduct research across multiple tabs and synthesize findings

• Interact with web applications using the same computer use capabilities as the core model

Combined with Sonnet 4.6's significantly improved computer use reliability and prompt injection resistance, Chrome integration becomes a viable tool for automating browser-based business processes.

API Pricing: What It Actually Means

Raw pricing tables obscure the real cost calculus. Here's how to think about it in practice.

Standard API pricing (Sonnet 4.6): $3 input / $15 output per million tokens

Batch API: ~50% discount vs. standard for asynchronous workloads. If your use case tolerates a processing delay of up to 24 hours (document processing, overnight analysis runs, bulk code review), Batch API halves your costs with zero code changes beyond setting the mode: batch parameter.

Fast Mode / Extended Thinking tradeoff: When you disable extended thinking on Sonnet 4.6, you get faster, cheaper responses that are still excellent for most tasks. Turn extended thinking on for complex reasoning tasks where the model needs to work through ambiguity systematically. The cost difference can be significant at scale — benchmark your specific workload rather than assuming one setting fits all.

Cost architecture recommendation:

• Use Haiku 4.5 for classification, routing, and simple extraction at high volume

• Use Sonnet 4.6 (thinking off) for standard coding and document tasks

• Use Sonnet 4.6 (thinking on) for complex agentic tasks and long-context reasoning

• Reserve Opus 4.6 for orchestration, deep refactoring, and multi-agent coordination

Q&A

Q: Should I migrate from Sonnet 4.5 to Sonnet 4.6 now?

Yes, immediately. Pricing is identical ($3/$15 per million tokens), and the model is better across every dimension. The only consideration is testing your specific evals before switching production traffic — but for most use cases, preference data shows Sonnet 4.6 wins significantly over 4.5.

Q: When should I use Opus 4.6 instead of Sonnet 4.6?

Use Opus when the task involves: coordinating multiple agents in a workflow, refactoring a codebase where a wrong assumption has cascading consequences, or problems where nuance and edge cases matter more than speed or cost. For most day-to-day development tasks, Sonnet 4.6 now hits Opus-level quality at Sonnet pricing.

Q: How do I install Claude Code without using NPM?

Use the native installers: curl -fsSL https://claude.ai/install.sh | sh on macOS/Linux, or winget install Anthropic.ClaudeCode on Windows. NPM installation is deprecated and will not receive future updates.

Q: What is Adaptive Thinking and how does it differ from Extended Thinking?

Extended Thinking is a user-controlled setting where you explicitly allocate a token budget for the model to reason step-by-step before answering. Adaptive Thinking is a model-level behavior (available on Sonnet 4.6 and Opus 4.6) where the model itself determines when deeper reasoning is warranted — you don't set a budget. For most API users, Adaptive Thinking requires no configuration changes and delivers better results on complex queries without the overhead of always-on Extended Thinking.

Q: What is context compaction and when does it activate?

Context compaction is a beta feature on the Claude Developer Platform that automatically summarizes older parts of a conversation as the context window fills up. Rather than hitting a hard cutoff, the model maintains an effective summary of earlier context, allowing very long agentic sessions to continue without restarting. It activates when the conversation approaches the context limit and is configurable via the API. It's particularly valuable for long Claude Code sessions and multi-turn document analysis.

Q: Is MCP configuration shared across Claude products?

Yes. MCP connectors set up in ~/.claude/mcp.json or configured in claude.ai are shared automatically across Claude Code, the web interface, and Claude in Excel. Configure once, use across all surfaces.

Q: What are the security implications of computer use in production?

The primary risk is prompt injection — malicious instructions hidden in web content that attempt to hijack the model's actions. Sonnet 4.6 shows major improvements in prompt injection resistance compared to Sonnet 4.5, performing similarly to Opus 4.6 on security evaluations. For production computer use deployments, follow Anthropic's guardrail documentation, operate in sandboxed environments where possible, and implement human confirmation checkpoints for irreversible actions.

Q: How does Claude Code's Agent Teams feature work under the hood?

When you issue a task that Claude Code determines can be parallelized, or when you explicitly request parallel execution, Claude Code spawns multiple agent instances, each with a focused scope. These agents run concurrently, each maintaining their own context window, and results are synthesized by a coordinator (typically Opus 4.6 in complex orchestrations, or handled by Claude Code's internal routing for simpler tasks). Each sub-agent has access to the same MCP connectors and codebase context as the primary session.

Q: Which model powers Claude in Excel?

Claude in Excel uses Claude Sonnet 4.6 by default as of the February 2026 update. Enterprise customers on Team and Enterprise plans can configure their preferred model through the Claude for Work admin settings.

Q: How do Claude Code plugins/skills relate to MCP connectors?

They solve different problems. MCP connectors give Claude access to external data sources and tools at runtime (Jira, Slack, GitHub, financial data APIs). Skills give Claude specialized behavioral instructions for how to approach and execute a class of tasks — like how to generate production-grade frontend code or how to write internal team communications in a consistent voice. In practice you'd use both: an MCP connector to pull data from a source, and a skill to ensure Claude formats and structures the output to your standards.

Q: Can I create private Claude Code skills for my team?

Yes. The plugin and skill system is fully open. You create a skills directory with the required structure, add SKILL.md files for each skill, and register your own marketplace. Teams can host private marketplaces in their own GitHub repos (public or private with appropriate access tokens) and install them in exactly the same way as public ones. The template/SKILL.md in the LAXIMA Skills repo is a good starting point for the required format.

Q: What's the difference between a system prompt and a user prompt, and when does it matter?

A system prompt is set by whoever configures the AI product or API call — it defines Claude's persona, constraints, and behavioral defaults before any user interaction. A user prompt is what you type in the chat or pass as the human turn in an API request. In Claude Code, skills function like dynamic system prompt additions: they inject instructions into Claude's context automatically based on the task at hand. The prompt library at laxima.tech/tools/library contains both user prompts (templates you fill in and send) and actual system prompts from major AI products — useful for understanding how those tools are configured at a foundational level.

Q: Is the 1M token context window available to all users?

The 1M token context window is in beta for both Sonnet 4.6 and Opus 4.6. It's available on the API and in Claude Code. Standard context limits may apply on the Free tier. Check claude.com/pricing for the current status of the beta rollout by plan.

Where to Go From Here

• Try Claude Sonnet 4.6 in Claude Code: claude config set model claude-sonnet-4-6

• API access: platform.claude.com — use model string claude-sonnet-4-6

• LAXIMA Skills — Claude Code plugin marketplace: github.com/laxima-tech/laxima-skills

• LAXIMA AI Prompt Library (60 free prompts): laxima.tech/tools/library

• Claude in Excel add-in: support.claude.com/en/articles/12650343

• System card and safety evaluations: anthropic.com/claude-sonnet-4-6-system-card

• MCP documentation: platform.claude.com/docs/en/agents-and-tools

• Adaptive thinking docs: platform.claude.com/docs/en/build-with-claude/adaptive-thinking

• Agent Skills standard: agentskills.io