What is Claude, and who is Anthropic?
Anthropic is an AI company founded in 2021 by Dario Amodei, Daniela Amodei, and several colleagues who previously worked at OpenAI. The company describes itself as focused on building reliable and steerable AI systems, and it has made safety research, system cards, and staged deployment a visible part of its product strategy.
Claude is Anthropic's flagship AI assistant and model family. You can use it for writing, research, coding, document analysis, data work, and many other text-and-image tasks. The name is a nod to Claude Shannon, whose work on information theory helped shape modern computing and communication.
Claude is often praised for careful writing and long-context analysis, but it is still a large language model. It can hallucinate, miss context, or sound more confident than the evidence supports. The useful way to think about Claude is not "safe, therefore always right." It is "a strong model family from a company that foregrounds safety and disclosure more than many competitors."
The Naming System
Claude's public model tiers use three names: Haiku, Sonnet, and Opus. They are not just marketing labels. They indicate a practical tradeoff.
- Haiku is the fast, lower-cost tier for high-volume or latency-sensitive work.
- Sonnet is the workhorse tier for most writing, coding, research, and business tasks.
- Opus is the highest-end public tier for complex reasoning, deeper code work, long-running agentic tasks, and hard analysis.
That hierarchy is still useful, but it is less absolute than it used to be. Newer smaller models can outperform older larger models on some tasks. Model choice now depends on the actual job: speed, price, context length, risk, and quality threshold.
The Current Public Lineup
Haiku 4.5
Haiku 4.5 is Anthropic's fast, cost-efficient model. Anthropic positions it as a small model with strong coding and computer-use performance, especially useful for scaled deployments where response time and cost matter.
It is the right tier for customer-facing chat, quick classification, routing, summarization, lightweight coding help, and multi-agent workflows where one stronger model delegates subtasks to cheaper, faster workers.
Who this is for: Developers and teams that need lots of AI calls at manageable cost, plus everyday users who want fast help on simpler tasks.
Sonnet 4.6
Sonnet 4.6 is the default workhorse for many Claude users. Anthropic says it improves coding, computer use, long-context reasoning, agent planning, knowledge work, and design. It also supports a 1-million-token context window in beta, which matters for long documents, large codebases, and extended project context.
For most professional work, Sonnet is the tier to start with. It is strong enough for serious writing, coding, research synthesis, document review, and planning, without the cost profile of Opus.
Who this is for: Most people. If you are using Claude for work and do not know which tier to choose, Sonnet is usually the first reasonable option.
Opus 4.7
Opus 4.7 is Anthropic's highest-end public model. It is aimed at harder coding, research, spreadsheet and document work, complex analysis, and longer agentic tasks where the cost of a weak answer is high.
Anthropic has also lowered the cost of Opus-class capability compared with earlier Opus pricing. That does not make Opus cheap, but it makes the top tier less reserved for only rare workloads. Pricing changes quickly, so production teams should always check Anthropic's current pricing page before building a business case.
Who this is for: Power users, developers, analysts, and teams doing work where accuracy, planning depth, or code reliability matters enough to justify the higher cost.
How You Access Claude
Claude is available through several routes.
Claude.ai is the consumer web app and the easiest starting point. Plan names, usage limits, and model availability change over time, so treat the app as the simplest access point rather than a stable statement of the full product lineup.
The Claude API is for developers who want to build Claude into products or workflows. API users pick specific model IDs, pay per token, and can integrate Claude into their own systems.
Cloud platforms including Amazon Bedrock, Google Cloud Vertex AI, and other enterprise channels make Claude available inside existing cloud procurement, security, and governance workflows.
Claude Code puts Claude into developer workflows through command-line and editor integrations. This matters because coding assistants are moving from "answer my question" toward "work with the project files, propose changes, and help verify them."
The pattern is clear: Claude is becoming less just a chat surface and more a set of models embedded into professional workflows.
Mythos Preview and Project Glasswing
Claude Mythos Preview is not a normal public tier. In April 2026, Anthropic announced Project Glasswing, a restricted defensive-security initiative powered by Claude Mythos Preview.
The reason Mythos matters is its cybersecurity capability. Anthropic says the model can help find and exploit software vulnerabilities at a level that changes the risk picture. Rather than release it broadly, Anthropic is providing restricted access to vetted security partners working on defensive hardening.
Mozilla has described using Claude Mythos Preview, alongside other models and its own security pipeline, to help identify and fix hundreds of Firefox security bugs in April 2026. That is the clearest public example of the double-edged nature of this capability: the same model that helps defenders find vulnerabilities could also help attackers if access were loose.
So when people ask "what is coming next for Claude," Mythos is the most important signal. Not because you should expect to use it in Claude.ai tomorrow, but because it shows where frontier models are headed: more capable, more specialized, and harder to release safely.
A Moving Target
Claude changes quickly. This article reflects Anthropic's lineup and public disclosures as of May 2026. If you are making a technical or purchasing decision, check Anthropic's model documentation, pricing page, and system cards before treating any model name, context limit, or price as fixed.
The deeper lesson is durable: model choice is no longer about picking "the best AI." It is about matching the model to the task, the risk, the budget, and the level of oversight you can provide.