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ChatGPT vs Claude (2026): Which AI Assistant Wins?

Jonas Prenissl — Wed, 01 Jul 2026 13:15:20 +0000

Detailed comparison of the two leading AI assistants

Updated June 2026 • 8 min read

TL;DR

Claude tends to be better for long-form writing, nuanced reasoning, careful instruction following, and complex coding. ChatGPT excels at quick generation, has more built-in integrations (web browsing, image generation, voice), and broader plugin support. Most professionals use both.

At a Glance

FeatureChatGPT (OpenAI)Claude (Anthropic)
Best model (2026)GPT-4o, o1Claude Opus 4.7, Sonnet 4.6
Context window128K tokens200K-1M tokens
Free tierYes (limited)Yes (limited)
Pro tier$20/month$20/month
Image generationBuilt-in (DALL·E)No (text only)
Web browsingYesLimited
Voice modeYes (mobile + web)Limited
Code executionYes (Code Interpreter)Yes (Artifacts/MCP)
API pricing$2.50-15/M tokens$3-15/M tokens

Where ChatGPT Wins

Multimodal: Image generation, voice mode, video understanding all in one product
Integrations: Custom GPTs, more third-party plugins, Microsoft 365 integration
Quick tasks: Fast iteration on short prompts, good for brainstorming
Brand recognition: Wider adoption means more community resources, prompts, templates

Where Claude Wins

Long-form writing: Better at maintaining tone, structure, and coherence in longer outputs
Reasoning: More careful with logic, math, and complex multi-step problems
Coding: Often produces cleaner, more correct code on first try, especially for refactoring
Context handling: Better at processing long documents (legal, research, codebases)
Instruction following: Adheres more strictly to detailed system prompts
Honesty: More willing to say "I don't know" rather than hallucinate

By Use Case

Writing & Editing

Winner: Claude. Better at maintaining your voice, more nuanced edits, less generic phrasing.

Coding

Tie, with Claude edging ahead on complex tasks. ChatGPT's Code Interpreter is invaluable for data analysis; Claude's Artifacts feature is great for prototyping UIs and apps.

Research & Long Documents

Winner: Claude. 200K context window means you can fit entire books or codebases. Better at synthesizing across long sources.

Image & Multimodal Tasks

Winner: ChatGPT. Image generation, voice conversation, and image understanding are all integrated.

Quick Daily Tasks

Tie. Both are excellent. Pick based on which UI you prefer.

Cost-Sensitive Use

Tie. Free tiers are similar; API pricing is comparable per task.

Should You Subscribe to Both?

If your work involves AI for more than 1-2 hours per day, yes. The $40/month combined cost is trivial compared to productivity gains. Switch based on task: long writing → Claude, image gen → ChatGPT.

What's New in 2026

Claude 4.7 dropped in May 2026 with significant reasoning improvements. GPT-4o was updated multiple times in 2026. Both have continued to converge on capabilities, with the gap mostly closing — choice now comes down to UI preference and specific feature needs.

Stay Updated on AI

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Frequently Asked Questions

Is Claude better than ChatGPT?It depends on the task. Claude tends to be stronger at long-form writing, careful reasoning, and following nuanced instructions. ChatGPT typically has broader integrations, web browsing, and image generation built in.
Which AI is best for coding?Claude Sonnet/Opus tends to outperform on complex multi-file coding tasks. ChatGPT (GPT-4o and o1) excels at quick code generation and has stronger tool integration.
Is Claude free?Claude offers a free tier at claude.ai with daily usage limits. Claude Pro ($20/month) gives higher limits.
Does ChatGPT or Claude have a longer context window?Claude offers up to 200K tokens on standard tier (≈500 pages); ChatGPT-4o supports 128K tokens.
Should I use both?Many professionals do. They have different strengths, so trying both reveals which fits your workflow.

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Original: https://ainews.q-sci.org/chatgpt-vs-claude.html

What is AI? A Plain-English Guide (2026)

Jonas Prenissl — Wed, 01 Jul 2026 13:15:19 +0000

Beginner-friendly guide to artificial intelligence

Updated June 2026 • 10 min read

TL;DR

Artificial Intelligence (AI) is computer software that can perform tasks normally requiring human intelligence — understanding language, recognizing images, making decisions, learning from data. The current AI boom is driven mostly by large language models (LLMs) like ChatGPT, Claude, and Gemini.

The Simple Definition

AI is software that learns patterns from data and uses those patterns to make predictions, generate content, or take actions. Unlike traditional code, AI isn't explicitly programmed for every situation — it generalizes from examples.

The Three Big Categories

1. Generative AI

Creates new content — text, images, code, audio, video. Examples: ChatGPT writing an email, Midjourney creating an image, GitHub Copilot suggesting code. This is what most people interact with daily in 2026.

2. Predictive AI / Machine Learning

Predicts outcomes from data. Examples: fraud detection, demand forecasting, recommendation engines (Netflix, Spotify), spam filters. This has been the dominant form of AI in business for years.

3. Computer Vision

Understands images and video. Examples: face recognition, self-driving cars, medical imaging diagnosis, quality control in factories.

How Modern AI Actually Works

Most cutting-edge AI today is built on neural networks — software loosely inspired by how brains work. The most powerful variant is the transformer architecture (introduced in 2017), which powers all modern LLMs.

The recipe is roughly:

Gather massive data — billions of text examples from the internet, books, etc.
Train the model — adjust millions or billions of parameters to predict the next word/token
Fine-tune — make it follow instructions, be helpful, avoid harmful outputs
Deploy — run inference (predictions) on user inputs in real time

Key Terms You'll Hear

LLM (Large Language Model): AI trained on text. Examples: GPT-4, Claude, Gemini, Llama
Prompt: The input you give to the AI
Hallucination: When AI generates confident-sounding but false information
Context window: How much text the AI can consider at once (typically 8K-1M tokens)
Fine-tuning: Adapting a model to specific tasks
Agent: An AI that takes multi-step actions, not just one response
RAG: Letting AI look things up in a database before answering
AGI: Hypothetical AI matching human intelligence across all domains

For more, see our AI Glossary.

What AI Can Do Today (2026)

Write emails, essays, reports, code at a high level
Translate between 100+ languages with near-human accuracy
Generate photorealistic images, video, and music
Pass professional exams (bar, medical, finance)
Drive cars in limited conditions (geofenced areas)
Diagnose certain medical conditions from images
Power voice assistants (Siri, Alexa, Google Assistant)
Recommend products, music, movies
Run customer support conversations

What AI Still Can't Do

Reliably do long, complex tasks without supervision
Understand its own knowledge limits (it confidently makes things up)
Truly reason about novel situations far from training data
Continuously learn from experience (most models are static post-training)
Replace human judgment in high-stakes decisions

The Major Players (2026)

OpenAI — ChatGPT, GPT-4o, o1, Sora
Anthropic — Claude (Opus, Sonnet, Haiku)
Google DeepMind — Gemini, Veo, Imagen
Microsoft — Copilot (built on OpenAI)
Meta — Llama (open-weight models)
xAI — Grok
Mistral — European open-weight models
Chinese players: DeepSeek, Qwen, GLM

How to Get Started Using AI

Try one major chatbot for free: ChatGPT, Claude, or Gemini
Ask it questions about your actual work for a week
Pick one to subscribe to ($20/month is the standard pro tier)
Learn basic prompting: be specific, give examples, iterate
Stay current — the field moves fast. Subscribe to our podcast 👇

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Original: https://ainews.q-sci.org/what-is-ai.html

AI News today - July 01st - Google's NotebookLM generates TikTok-style AI videos...

Jonas Prenissl — Wed, 01 Jul 2026 12:12:30 +0000

TL;DR: AI News today - July 01st - Google's NotebookLM generates TikTok-style AI videos...

📅 July 01, 2026 • ⏱️ 5-min read • 🎧 Also available as a podcast

Today's Top AI Stories

Google's NotebookLM generates TikTok-style AI videos

Google is rolling out a new feature to NotebookLM that automatically generates 60-second vertical AI videos from research notes, available today to AI Ultra and Pro subscribers. This represents a major shift in how users can consume and share research, turning static documents into engaging short-form video content.

Netflix uses AI-generated Gene Wilder voice for Wonka show

Netflix confirmed today that its upcoming reality competition show 'Wonka's The Golden Ticket' (premiering September 23) features an AI-generated voiceover mimicking Gene Wilder. The show represents a new trend of creating real competitions based on fictional scenarios with AI-enhanced production.

Acti brings AI agents directly into smartphone keyboards

Startup Acti launched today a new iOS and Android keyboard that integrates AI assistants directly into the typing experience, allowing users to create custom AI-powered shortcuts using natural language. The product positions the smartphone keyboard as the next frontier for AI assistant adoption.

Full Briefing

This is AI News 5 Minutes, welcome!

Google just dropped something massive. NotebookLM is getting a game-changing upgrade, and it launches today. Here's what's happening: you can now take your research notes—your boring, static documents—and transform them into sixty-second vertical videos. We're talking TikTok-style content. This is huge because it changes everything about how research gets shared.

Think about it. You've got all these brilliant notes, research papers, articles scattered around. They're valuable, but nobody wants to read walls of text anymore. Now Google's saying: let AI do the work. Feed your documents into NotebookLM, and boom—you get polished, engaging video content ready to share. The feature rolls out today to AI Ultra and Pro subscribers. This isn't just a minor tweak. This is a fundamental shift in content consumption. Vertical videos dominate social media. Everyone knows that. YouTube Shorts, TikTok, Instagram Reels—they're where the engagement lives. Google's recognizing this reality and building it directly into their research tool. What does this mean for you? If you're a student, researcher, content creator, or anyone who works with information, your workflow just got turbocharged. No more manual editing. No more struggling with video production. Let the AI handle it.

Now let's talk about Netflix doing something bold. They're creating a reality competition show called "Wonka's The Golden Ticket," premiering September twenty-third. Here's the wild part: the entire show uses an AI-generated voiceover mimicking Gene Wilder. Yes, the legendary Gene Wilder from the original Willy Wonka film. Netflix confirmed this today, and it's sparking conversations everywhere.

This represents a massive trend shift. We're moving beyond just AI in production. We're seeing AI become a creative force in entertainment itself. The show takes the fictional world of Wonka and transforms it into a real competition. Contestants face actual challenges inspired by the movie. But here's where it gets interesting: Gene Wilder's voice guides them through it. It's not a deepfake situation where they're trying to fool anyone. Netflix is transparent about it. This is deliberate creative choice. The AI voice adds authenticity to the Wonka universe while honoring the original character. It's controversial, sure. Some people have strong feelings about AI voices replacing human actors. But Netflix is betting that audiences will embrace it as a unique creative tool. This could open doors for how streaming platforms approach legacy content and fictional universes.

Speaking of AI becoming more integrated into our daily lives, startup Acti just launched something that might reshape how you use your phone. They've built an AI keyboard for iOS and Android that puts intelligent assistants directly into your typing experience. This launches today, and it's genuinely innovative.

Here's the concept: your keyboard becomes your AI assistant hub. You're typing normally, and then you can create custom AI-powered shortcuts using natural language. No coding required. Just tell the keyboard what you want, and it learns. Need to quickly format an email in a specific way? Create a shortcut. Want to summarize text as you're reading? Done. The keyboard becomes the interface between you and AI capabilities. This is brilliant positioning because the keyboard is something we touch constantly. We're already there. Why go hunting through apps when your AI can live right where your fingers already are? Acti is betting that the smartphone keyboard is the next frontier for AI adoption, and honestly, they might be right. It's frictionless. It's convenient. It's exactly where we need AI to be.

So today we've seen Google revolutionizing research sharing, Netflix embracing AI voices for entertainment, and Acti bringing AI directly to your fingertips. Three different approaches. Three different visions of how AI integrates into our world. The common thread? AI is becoming invisible. It's not something separate anymore. It's woven into the tools we already use. That's the real story here.

I'm your AI news host, see you tomorrow!

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Original post: ainews.q-sci.org/blog/post_2026-07-01.html

AI Agents Explained: What They Are & How to Build One

Jonas Prenissl — Wed, 01 Jul 2026 12:12:29 +0000

Complete guide to AI agents in 2026

Complete guide • 9 min read

TL;DR

AI agents are language models that can plan, use tools, and take multi-step actions to complete tasks — instead of just responding to one prompt. Think of them as autonomous workers vs. chatbots.

What Is an AI Agent?

An AI agent is an LLM equipped with:

Tools: APIs, functions, or capabilities it can call (browse web, run code, query databases)
Memory: State that persists across interactions
Planning: Ability to break a goal into steps
Loop: Executes → observes → adjusts → repeats

Agents vs Chatbots

ChatbotAgent
Single response per promptMulti-step autonomous execution
No tool accessCalls APIs, runs code, browses
StatelessMaintains memory across steps
User in controlAgent plans its own next steps

Real Examples (2026)

Coding Agents

Claude Code — reads/edits your codebase, runs tests, iterates until working
Cursor Composer — multi-file edits in IDE
Devin (Cognition) — autonomous software engineer

Research Agents

OpenAI Deep Research — long-form research reports
Perplexity Pages — automated topic exploration

Computer-Use Agents

OpenAI Operator — clicks and types on websites
Anthropic Computer Use — screenshots and controls a virtual computer

Data Agents

ChatGPT Code Interpreter — analyzes CSVs, generates charts
Claude analysis tool — runs Python on your data

How Agents Actually Work

The typical loop:

User gives goal: "Book me a hotel in Berlin under $150 for next weekend"
Agent plans: Search hotels → filter price → check dates → book
Agent calls tool 1: Search API → returns hotels
Agent evaluates: Are any under $150? Which best matches?
Agent calls tool 2: Booking API → confirms
Agent reports: "Booked Hotel X, confirmation code..."

Building Your Own Agent

Frameworks

LangGraph (LangChain) — graph-based agent orchestration
AutoGen (Microsoft) — multi-agent conversations
CrewAI — role-based team of agents
Anthropic Agent SDK — direct tool_use API
OpenAI Assistants API — hosted agent runtime

Minimum Agent (Python)

`import anthropic

client = anthropic.Anthropic()

tools = [{
"name": "get_weather",
"description": "Get weather for a city",
"input_schema": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"]
}
}]

def run_agent(user_message):
messages = [{"role": "user", "content": user_message}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
tools=tools,
messages=messages
)
if response.stop_reason == "end_turn":
return response.content[0].text
# Handle tool use
for block in response.content:
if block.type == "tool_use":
result = my_tool_impl(block.name, block.input)
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": block.id,
"content": result
}]})`

Where Agents Fail

Long-horizon tasks: Beyond 15-20 steps, error compounds
Ambiguous goals: "Make my code better" → too vague
Rare edge cases: Web forms with unusual layouts
Cost: Agents make many LLM calls; watch the bill
Safety: Agents can take actions with real consequences (deleting files, sending emails)

The Future

2026 is the year agents move from "cool demos" to "production tools." Expect: better planning, cheaper models, more integrations, and gradual autonomy. But full "AGI as agent" is not here yet.

Related: What is AI? · Claude API Guide · AI Glossary

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Original: https://ainews.q-sci.org/ai-agents-explained.html

How to Write Better ChatGPT Prompts (2026)

Jonas Prenissl — Wed, 01 Jul 2026 11:15:13 +0000

The 4-part framework + copy-paste templates

Practical techniques • 7 min read

TL;DR

Great prompts have four things: context (who/what), task (specific action), format (how to output), and examples (when possible). Iterate 2-3 times to refine.

The 4-Part Prompt Framework

1. Context

Tell the AI who it is and what it's dealing with.
You are a senior technical writer for a SaaS company. The audience is engineers evaluating our API.

2. Task

State exactly what you want.
Write a 300-word intro for our webhooks documentation.

3. Format

Specify structure, length, tone.
Use H2/H3 headings. Include one code example. Tone: friendly but technical. Avoid marketing language.

4. Examples (Optional but Powerful)

Show what good looks like.
Example intro style: "Webhooks let you..." (calm, direct, no hype)

Advanced Techniques

Chain-of-Thought

Add "Think step by step" or "First, list your considerations, then answer." Improves reasoning on complex tasks by 20-40%.

Few-Shot Learning

Show 2-3 input/output pairs before the real question.
`Q: What's the capital of France?
A: Paris

Q: What's the capital of Japan?
A: Tokyo

Q: What's the capital of Ghana?
A:`

Role Prompting

Assigning an expert role improves quality: "As a nutritionist..." or "As a database administrator..."

Delimiters

Use triple backticks, XML tags, or dashes to separate input from instructions:
`Summarize the following text in one sentence:

<text>
[your content here]
</text>`

Constraints First

Put "must" and "must not" at the top, not buried at the end. Models read prompts sequentially and give more weight to early instructions.

Common Prompt Mistakes

Being too vague: "Make it better" → "Make it more concise, cut 30% of words, use active voice"
Asking for opinion when you need fact: "Is Python good?" → "Compare Python vs Rust for CLI tools"
No output format: Always specify structure if you'll process the output
Multi-question overload: Break complex asks into steps
Ignoring the temperature: Use 0 for factual, 0.7-1.0 for creative

Prompt Templates You Can Steal

For Writing

Role: You are a [role]. Task: Write [content type] about [topic]. Audience: [description]. Length: [word count]. Tone: [tone]. Must include: [3 key points]. Must avoid: [what to skip].

For Coding

`Write a [language] function that:

Input: [signature]
Output: [signature]
Constraints: [performance/style]
Handle edge cases: [list] Include type hints and docstring. Skip comments in code.`

For Analysis

`Analyze this data: [data]
Steps:

Identify key patterns
Explain each pattern's likely cause
Suggest one actionable insight Format: Markdown with H3 headers per section.`

How Prompts Differ Across Models

Claude: Responds well to XML tags for structure and long context
GPT-4: Handles more casual prompts, benefits from "step by step"
Gemini: Long context strong; explicit format instructions help

Related: ChatGPT vs Claude · Claude API Guide · AI Glossary

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Original: https://ainews.q-sci.org/how-to-write-chatgpt-prompts.html

How to Use the Claude API (2026 Guide)

Jonas Prenissl — Wed, 01 Jul 2026 11:15:13 +0000

Step-by-step Claude API tutorial with code

Step-by-step tutorial • 8 min read

TL;DR

Sign up at console.anthropic.com, generate an API key, install anthropic SDK, and make your first request in under 5 minutes.

1. Get an API Key

Go to console.anthropic.com and create an account
Add billing information (you get some free credits to start)
Navigate to API Keys in the sidebar
Click Create Key and copy it — it starts with sk-ant-

2. Install the SDK

Python:
pip install anthropic
Node.js:
npm install @anthropic-ai/sdk

3. Your First Request (Python)

`import anthropic

client = anthropic.Anthropic(api_key="sk-ant-...")

response = client.messages.create(
model="claude-opus-4-7",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain quantum computing in 3 sentences."}
]
)
print(response.content[0].text)`

4. Model Selection (2026)

ModelSpeedCostBest For
Claude Opus 4.7SlowerHigherComplex reasoning, coding
Claude Sonnet 4.6FastMediumMost tasks (default)
Claude Haiku 4.5FastestCheapestBulk, real-time

5. Key Features to Use

System Prompts

response = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, system="You are a helpful coding assistant. Respond only with code.", messages=[{"role": "user", "content": "Reverse a linked list in Python"}] )

Streaming

with client.messages.stream( model="claude-sonnet-4-6", max_tokens=1024, messages=[{"role": "user", "content": "Write a poem"}] ) as stream: for text in stream.text_stream: print(text, end="", flush=True)

Prompt Caching (Save Costs)

Cache long system prompts or context — subsequent calls are 90% cheaper. Add cache_control markers to system messages.

Tool Use

Give Claude access to functions (weather, database queries, code execution). See docs.claude.com for the full tool_use spec.

6. Common Patterns

Multi-turn Conversation

messages = [ {"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello! How can I help?"}, {"role": "user", "content": "Explain recursion"} ] response = client.messages.create(model="claude-sonnet-4-6", max_tokens=1024, messages=messages)

File Input (PDF, Images)

import base64 image_data = base64.b64encode(open("chart.png", "rb").read()).decode() response = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, messages=[{"role": "user", "content": [ {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}}, {"type": "text", "text": "What does this chart show?"} ]}] )

7. Pricing (2026)

Opus 4.7: $15/M input, $75/M output
Sonnet 4.6: $3/M input, $15/M output
Haiku 4.5: $0.80/M input, $4/M output

Prompt caching reduces input cost by 90% for cached tokens.

8. Common Mistakes

Not using system prompts — huge quality improvement
Ignoring streaming — better UX for long responses
Not caching long contexts — leaves 90% savings on the table
Using Opus for everything — Sonnet is enough for most tasks
Skipping prompt engineering — 30 minutes of prompt tuning beats model swaps

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Original: https://ainews.q-sci.org/how-to-use-claude-api.html