AI CAPABILITY • LANDSCAPE
AI Landscape Navigator
The landscape is exploding. The gap between movers and waiters is widening.
More capability ships quarterly than used to ship in years. New models, tools, and platforms appear weekly. Competitive advantage is materialising now – not “someday.”
This living reference helps you understand what's out there, what matters, and where we stand. Not exhaustive – navigational.
A Living Reference
This page is updated as the landscape evolves. It reflects our current understanding and experience, not comprehensive market research. We include tools we've used, evaluated, or tracked closely. Last updated: April 2026.
The AI models powering everything — who builds them, what they’re good at, and how they compare.
ANTHROPIC
Claude models
Anthropic now leads OpenAI on annualised revenue: run-rate moved from $9B at end of 2025 to $30B in 2026, with $1M+ customers doubling to over 1,000 in under two months. The signal underneath: practitioners doing actual work pay for Claude. Opus 4.7 (April 2026) shipped at the same $5/$25 pricing as 4.6, with a vision jump (98.5% XBOW), a new xhigh effort tier, and the behavioural shift toward more literal instruction-following. Claude Code remains the practitioner-grade harness; Claude Design (April 2026) is now bundled into Pro and Max.
Examples: Claude Opus 4.7, Claude Sonnet 4.6, Claude Haiku 4.5
Strengths:
- +Frontier reasoning, vision, and document handling (Opus 4.7)
- +Practitioner-grade tools: Claude Code, Claude Design (bundled), Routines, Skills, MCPs
- +1M token context window; xhigh effort tier; Task Budgets (beta)
- +$30B ARR ahead of OpenAI; $1M+ customers >1,000
Considerations:
- •April 1 2026 supply-chain incident (Claude Code auto-update shipped a hostile package for 3 hours)
- •Premium pricing for frontier models
- •Rate limits tightened in April; some early Claude Design users lost projects
Our view: Our primary platform. We run Claude Code as our business operations hub, orchestrating strategy, research, delivery, and knowledge management daily. The April 2026 product wave (Opus 4.7 + Claude Design + Routines + desktop redesign) moved Claude from "smart assistant" to "practitioner-grade work platform."
OPENAI
GPT models & ChatGPT
OpenAI pioneered the current AI era and continues its strategic pivot to work AI. GPT-5.5 (late April 2026) is broadly comparable to Opus 4.7 on most everyday tasks and slightly cheaper. GPT Image 2 (mid-April 2026) takes a noticeable image-quality leap and now renders legible text inside images. The "AGI Deployment" division (renamed March 2026 from product), the Sora shutdown, and the Codex feature wave all point the same way: knowledge work automation is now the singular focus. Notable: Anthropic overtook OpenAI on annualised revenue in April 2026, the first time the relative positioning has flipped.
Examples: GPT-5.5, GPT Image 2, Codex, ChatGPT Plus
Strengths:
- +Largest consumer ecosystem and integrations
- +GPT-5.5: comparable to Opus 4.7 on everyday tasks, slightly cheaper
- +GPT Image 2: legible text in images (signage, slide labels, packaging)
- +Codex April update: computer use on Mac, in-app browser, GPT Image 1.5 native, monothread heartbeats
Considerations:
- •Anthropic now ahead on ARR ($30B vs OpenAI $25B); positioning is reversed
- •Sora shutdown signalled compute scarcity trade-offs
- •Microsoft dependency remains a concentration risk
Our view: The ecosystem leader on consumer reach. GPT-5.5's price-performance makes multi-model routing compelling — pair Opus 4.7 for highest-stakes work with GPT-5.5 for cost-sensitive routine tasks. GPT Image 2 is the most useful single OpenAI release of April for solo operators making marketing visuals.
Gemini models
Google brings deep AI research heritage and integration with Google ecosystem. Gemini models are multimodal from the ground up, with strong reasoning and long context capabilities.
Examples: Gemini 3 Pro, Gemini 3 Flash, Gemini 2.5 Pro
Strengths:
- +Native multimodality (text, image, video)
- +Google ecosystem integration
- +Very long context windows
- +Strong research foundation
Considerations:
- •Availability varies by region
- •Enterprise features still maturing
- •Less established in coding tasks
Our view: Strong option for multimodal and Google-integrated workflows.
XAI
Grok models
Elon Musk's AI venture, integrated with X (Twitter). Grok models are designed to be more direct and less filtered than competitors, with real-time access to X platform data. In March 2026, SpaceX filed for a $75B IPO with xAI merger implications — if completed, this would create an AI+space conglomerate with massive compute resources. Organisational fragility remains a factor: 9 of the original 11 co-founders have departed, and xAI has undertaken a ground-up infrastructure rebuild.
Examples: Grok 4, Grok 4 Heavy, Grok 4 Fast
Strengths:
- +Real-time information from X
- +SpaceX IPO filing ($75B target) could unlock massive capital
- +Less guardrails on topics
- +Fast iteration pace
Considerations:
- •Limited enterprise features
- •Tied to X ecosystem
- •Significant organisational fragility (9/11 co-founders departed)
- •IPO + merger complexity adds uncertainty
Our view: The SpaceX IPO changes the calculus — if completed, xAI gains access to enormous capital. But organisational fragility and the infrastructure rebuild still introduce real vendor risk. Monitor, but don't depend on.
META
Llama models (open-source)
Meta's open-source approach has democratised access to capable models. Llama can be run locally or on private infrastructure, offering control and privacy that hosted APIs cannot.
Examples: Llama 4 Maverick, Llama 4 Scout, Llama 4 Behemoth (preview)
Strengths:
- +Open source and customisable
- +Can run locally/privately
- +No per-token API costs
- +Growing ecosystem
Considerations:
- •Requires technical expertise to deploy
- •Smaller models than frontier APIs
- •Self-managed infrastructure
Our view: Important for privacy-sensitive deployments and organisations with technical capability.
MISTRAL
European AI models
European-founded AI company offering competitive models with strong performance-to-cost ratios. Open-weight models available for self-hosting, with API access for convenience. Le Chat Pro is one of the privacy-first tools commonly used on the "private side" of a Public/Private wall for solo regulated practices.
Examples: Mistral Large 3, Mistral Medium 3, Mistral Small 3.1, Le Chat Pro
Strengths:
- +European data sovereignty option
- +Strong price/performance
- +Open-weight models available
- +Le Chat Pro: privacy-first option for client-confidential work
Considerations:
- •Smaller ecosystem than US providers
- •Enterprise features still developing
Our view: Good option for European data sovereignty requirements. Le Chat Pro features prominently on the private side of the Public/Private wall pattern (see /ai/foundation).
APPLE
On-device AI + Private Cloud Compute
Apple announced its CEO succession in April 2026: hardware VP John Ternus replaces Tim Cook (rather than software-side or COO Jeff Williams). The signal is structural: Apple is betting on on-device silicon plus Private Cloud Compute, not the frontier-lab race. Apple Foundation Models run inside the device for most tasks; harder workloads route to Apple's own private cloud with verifiable guarantees that data stays out of training. For solo practitioners handling protected client data, this is one of the most consequential strategic signals of 2026.
Examples: Apple Foundation Models, Apple Intelligence, Private Cloud Compute
Strengths:
- +On-device by default for most tasks (privacy by architecture)
- +Private Cloud Compute with verifiable hardware-rooted guarantees
- +Tight integration across Apple ecosystem (iOS, macOS, iCloud)
- +Hardware-first strategic positioning under Ternus
Considerations:
- •Apple ecosystem only
- •Less raw frontier capability than dedicated lab models
- •Still relatively new vs. Anthropic / OpenAI / Google
Our view: Watch closely. The on-device AI path becomes more compelling every quarter, particularly for solo regulated practitioners and personal-life users where privacy and offline capability matter. Apple Intelligence is a natural complement to Lumo, Mistral Le Chat, and Maple on the private side of a Public/Private wall.
DEEPSEEK
Chinese frontier AI at fraction of the cost
DeepSeek shook the AI industry by producing frontier-competitive models at a fraction of US lab costs. DeepSeek V4 shipped on 27 April 2026 in Pro and Flash variants, priced at less than one-seventh the cost of Opus 4.6 for roughly one-generation-behind capability. R1 (reasoning) matches earlier o1 performance; V3 rivals GPT-4o. The arithmetic is now unambiguous: for routine tasks where "good enough" is genuinely good enough, DeepSeek changes the cost calculus.
Examples: DeepSeek V4 (Pro and Flash), V3, R1
Strengths:
- +V4 ships at <1/7th the cost of Opus 4.6 for one-generation-behind capability
- +Open-weight models available (R1, V3)
- +Efficiency breakthroughs in training methodology
- +Strong coding and mathematical reasoning; natively multimodal
Considerations:
- •Chinese company — data sovereignty concerns for some organisations
- •API reliability and availability can vary
- •Censorship on certain topics (Chinese regulatory compliance)
- •Rapidly evolving — model versions shift fast
Our view: The biggest disruption in AI economics since GPT-3. DeepSeek proved that frontier capability doesn't require frontier budgets. Essential for multi-model strategy — particularly for cost-sensitive workloads where R1 or V3 can match more expensive alternatives.
How We Navigate This
With so many options, how do you choose? Here's our approach.
Start with the Problem
Don't start with “what AI should we use?” Start with “what problem are we solving?” The tool follows from the task, not the other way around.
Favour Simplicity
The simplest tool that solves the problem is usually the right choice. Complexity has ongoing costs. Start simple; add sophistication when you hit limits.
Build for Portability
The landscape changes fast. Avoid deep lock-in where you can. Use standards (MCP, OpenAI-compatible APIs) that let you switch if better options emerge.
Test with Real Work
Demos impress; production reveals. Before committing, test tools on your actual tasks. What works in a demo may struggle with your specific context.
What's Not Here
Comprehensive Coverage
This isn't a complete market survey. We focus on tools we've used or seriously evaluated. Many good options aren't listed because we haven't worked with them.
Full Enterprise Stack
We cover M365 Copilot and Graph, but not the full enterprise AI stack (Copilot Studio, Power Platform AI, Salesforce Einstein, ServiceNow, etc.). These require enterprise-specific context.
Hardware & Infrastructure
GPU providers, cloud infrastructure (AWS Bedrock, Azure AI, GCP Vertex), and on-premise deployment options. These require infrastructure-specific context beyond this navigator.
Pricing Details
Pricing changes frequently. We mention pricing considerations but don't list specific prices. Check provider websites for current rates.
From Landscape to Practice
Understanding the landscape is step one. Making it work for your organisation is where we help.
Right-Sized Stack
What combination of these tools makes sense for your organisation type?
Adoption Journey
Where are you on the spectrum from locked-out to power user?
Context Engineering
The right information at the right time. How to design systems that give AI what it needs.
Learn more →Agents & Orchestration
One agent, infinite expertise. Skills-based AI systems that compound value.
Learn more →AI Skills & Fluency
The bottleneck isn't tools – it's people and culture. Building genuine capability.
Learn more →Why Timing Matters
The landscape isn't just changing – the pace of change is accelerating.
Clock Speed Reality
Features ship faster than conferences can announce them. More capability is shipping quarterly than organisations used to deliver in 5-6 years of traditional technology change.
Leaders Pulling Ahead
The gap between organisations that “get” AI and those still experimenting is widening. Not because technology is inaccessible – everyone has access now – but because execution speed is separating leaders from laggards. Healthcare illustrates the pattern: 81% of US doctors now use AI (doubled since 2023), but only 17% for diagnosis – adoption starts with documentation and research, core professional judgment comes last. The same pattern is playing out across every sector.
Model Commoditisation
The models themselves are increasingly commoditised. GPT 5.4 matches frontier capability at half the price. Your competitive advantage isn't which model you use – it's how quickly you build capability around it. Multi-model strategies are now the norm: the average user works across 3.5 different models, choosing the right tool for each task. This is an economic imperative, not just a technical preference.
Labs Eating the App Layer
AI labs are moving down-stack. Code review, security scanning, meetings — functions that were standalone products are being absorbed into lab offerings. The revenue gap is narrowing (Anthropic $19B vs OpenAI $25B ARR; Cursor at $2B). Platform risk is real: if you build on a capability a lab is likely to bundle, plan accordingly.
Need Help Navigating?
The landscape is overwhelming. We've been navigating it daily. Let's talk about what makes sense for your situation.