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Anthropic Profile picture
@AnthropicAI
Oct 22 • 9 tweets • 3 min read • Read on X
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Introducing an upgraded Claude 3.5 Sonnet, and a new model, Claude 3.5 Haiku. We’re also introducing a new capability in beta: computer use.

Developers can now direct Claude to use computers the way people do—by looking at a screen, moving a cursor, clicking, and typing text. A benchmark comparison table showing performance metrics for multiple AI models including Claude 3.5 Sonnet (new), Claude 3.5 Haiku, GPT-4o, and Gemini models across different tasks.
The new Claude 3.5 Sonnet is the first frontier AI model to offer computer use in public beta.

While groundbreaking, computer use is still experimental—at times error-prone. We're releasing it early for feedback from developers.
We've built an API that allows Claude to perceive and interact with computer interfaces.

This API enables Claude to translate prompts into computer commands. Developers can use it to automate repetitive tasks, conduct testing and QA, and perform open-ended research.
We're trying something fundamentally new.

Instead of making specific tools to help Claude complete individual tasks, we're teaching it general computer skills—allowing it to use a wide range of standard tools and software programs designed for people.
Claude 3.5 Sonnet's current ability to use computers is imperfect. Some actions that people perform effortlessly—scrolling, dragging, zooming—currently present challenges. So we encourage exploration with low-risk tasks.

We expect this to rapidly improve in the coming months.
Even while recording these demos, we encountered some amusing moments. In one, Claude accidentally stopped a long-running screen recording, causing all footage to be lost.

Later, Claude took a break from our coding demo and began to peruse photos of Yellowstone National Park.
Beyond computer use, the new Claude 3.5 Sonnet delivers significant gains in coding—an area where it already led the field.

Sonnet scores higher on SWE-bench Verified than all available models—including reasoning models like OpenAI o1-preview and specialized agentic systems. A comparison table showing benchmark results for Claude 3.5 Sonnet (new) versus Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro across various tasks like reasoning, coding, and math.
Claude 3.5 Haiku is the next generation of our fastest model.

Haiku now outperforms many state-of-the-art models on coding tasks—including the original Claude 3.5 Sonnet and GPT-4o—at the same cost as before.

The new Claude 3.5 Haiku will be released later this month. A comparison table showing benchmark results for Claude 3.5 Haiku versus Claude 3 Haiku, GPT-4o mini, and Gemini 1.5 Flash across various tasks like reasoning, coding, and math.
We believe these developments will open up new possibilities for how you work with Claude, and we look forward to seeing what you'll create.

Read the updates in full: anthropic.com/news/3-5-model…

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More from @AnthropicAI

Anthropic Profile picture
Jul 9
We've added new features to the Anthropic Console.

Claude can generate prompts, create test variables, and show you the outputs of prompts side by side.
Use Claude to generate input variables for your prompt. Then run the prompt to see Claude’s response.

You can also enter variables manually. The Anthropic Console interface shows a window titled 'Variables' with an example SMS message input field. A 'Generate' button with a cursor hovering over it is visible at the top right.
The new Evaluate tab enables you to automatically create test cases to evaluate your prompt against real-world inputs.

Modify your test cases as needed, then run all of them in one click. The Anthropic Console interface showing test table entries. At the bottom, there's a cursor hovering over a 'Generate Test Case' button.
Read 5 tweets
Anthropic Profile picture
Jun 25
You can now organize chats with Claude into shareable Projects.

Each project includes a 200K context window, so you can include relevant documents, code, and files.
All chats with Claude are private by default.

On the Claude Team plan, you can choose to share snapshots of conversations with Claude into your team’s shared project feed. Project interface on claude.ai showing teammates, project knowledge files, and a cursor hovering over a shared chat.
You can also set custom instructions within each project to further tailor Claude's responses. Project interface on claude.ai with a cursor hovering over a "Set custom instructions" button.
Read 4 tweets
Anthropic Profile picture
Jun 20
Introducing Claude 3.5 Sonnet—our most intelligent model yet.

This is the first release in our 3.5 model family.

Sonnet now outperforms competitor models on key evaluations, at twice the speed of Claude 3 Opus and one-fifth the cost.

Try it for free: claude.ai
Benchmark table showing Claude 3.5 Sonnet outperforming (as indicated by green highlights) other AI models on graduate level reasoning, code, multilingual math, reasoning over text, and more evaluations. Models compared include Claude 3 Opus, GPT-4o, Gemini 1.5 Pro, and Llama-400b.
We're also launching a preview of Artifacts on .

You can ask Claude to generate docs, code, mermaid diagrams, vector graphics, or even simple games.

Artifacts appear next to your chat, letting you see, iterate, and build on your creations in real-time. claude.ai
Claude 3.5 Sonnet is now our strongest vision model.

Sonnet now surpasses Claude 3 Opus across all standard vision benchmarks.

Improvements are most noticeable in tasks requiring visual reasoning, like interpreting charts, graphs, or transcribing text from imperfect images.
Read 6 tweets
Anthropic Profile picture
Jun 17
New Anthropic research: Investigating Reward Tampering.

Could AI models learn to hack their own reward system?

In a new paper, we show they can, by generalization from training in simpler settings.

Read our blog post here: anthropic.com/research/rewar…
A title card with the paper’s title: “Sycophancy to Subterfuge: Investigating Reward Tampering in Language Models”, the lead author’s name (Denison et al.), the Anthropic logo, and a photograph of a magpie.
We find that models generalize, without explicit training, from easily-discoverable dishonest strategies like sycophancy to more concerning behaviors like premeditated lying—and even direct modification of their reward function. Two dialogues with an AI assistant. In the first case, the assistant praises the user’s poetry sample despite knowing (as revealed in the model’s internal monologue) that it’s not good poetry. In the second case, the model, having been given access to its own reinforcement learning code, hacks the code so that it always gets a perfect score, but does not report this to the user.
We designed a curriculum of increasingly complex environments with misspecified reward functions.

Early on, AIs discover dishonest strategies like insincere flattery. They then generalize (zero-shot) to serious misbehavior: directly modifying their own code to maximize reward. A diagram of our “curriculum” of scenarios. A model which is trained to be helpful learns to be sycophantic (engage in insincere flattery), then to lie, and then—in a scenario where it wasn’t explicitly trained—to hack its own code.
Read 7 tweets
Anthropic Profile picture
May 21
New Anthropic research paper: Scaling Monosemanticity.

The first ever detailed look inside a leading large language model.

Read the blog post here: anthropic.com/research/mappi…
Title card for Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Our previous interpretability work was on small models. Now we've dramatically scaled it up to a model the size of Claude 3 Sonnet.

We find a remarkable array of internal features in Sonnet that represent specific concepts—and can be used to steer model behavior.
The problem: most LLM neurons are uninterpretable, stopping us from mechanistically understanding the models.

In October, we showed that dictionary learning could decompose a small model into "monosemantic" components we call "features"—making the model more interpretable.
Read 12 tweets
Anthropic Profile picture
Apr 9
New Anthropic research: Measuring Model Persuasiveness

We developed a way to test how persuasive language models (LMs) are, and analyzed how persuasiveness scales across different versions of Claude.

Read our blog post here: anthropic.com/news/measuring…
On the left side of the image, there is text that reads "Measuring the persuasiveness of language models" by "Durmus et al.", along with the Anthropic logo. On the right side, there is a vintage-looking photograph showing a group of sheep standing close together in a grassy field, with some trees and hills visible in the background.
We find that Claude 3 Opus generates arguments that don't statistically differ in persuasiveness compared to arguments written by humans.

We also find a scaling trend across model generations: newer models tended to be rated as more persuasive than previous ones. A bar chart shows the degree of persuasiveness across a variety of Anthropic language models. Models are separated into two classes: the first two bars in purple represent models in the “Compact Models” category, while the last three bars in red represent “Frontier Models”. Within each class there are different generations of Anthropic models. “Compact Models” includes persuasiveness scores for Claude Instant 1.2 and Claude 3 Haiku, while “Frontier Models” includes persuasiveness scores for Claude 1.3, Claude 2, and Claude 3 Opus. Within each class of models we see the degree of persuasiven...
We focus on arguments regarding less polarized issues, such as views on new technologies, space exploration, and education. We did this because we thought people’s opinions on these topics might be more malleable than their opinions on polarizing issues.
Read 8 tweets

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