GPT-6 Astra: Features, Performance Benchmarks, Pricing & How to Access

GPT-6 Astra: Features, Performance Benchmarks, Pricing & How to Access

On AI

OpenAI just launched GPT-6 Astra, and they're calling it the smartest and safest model in the world. Whether that claim holds up depends on what you actually need it to do.

Astra enters a crowded field where Claude Fable 5.1 (launched September 1, 2026) and Claude Opus 5 have already set high bars for coding and autonomous work. The headline numbers are genuinely impressive—but context matters more than raw scores.

The main claims about this model look solid on paper.

Astra hit maximum scores on FrontierMath Tier 4 with 97.6%, maxed out ARC-AGI-3 at 99.9% (using OpenAI's custom adapter), and achieved a perfect 100% on ExploitBench. It also set new records for computer use, hitting 72.6% on OSWorld 2.0 while completing tasks roughly 47% faster than its predecessor, GPT-5.6 Sol.

Both ARC-AGI-3 and FrontierMath Tier 4 are specifically designed to stay ahead of AI capabilities. Hitting maximum scores on tests built to resist saturation isn't the same as just topping a standard leaderboard—it signals a qualitative leap forward.

This article covers everything new in GPT-6 Astra: what it can actually do, how it performs in real benchmarks, and whether it makes sense for your workflow.

Want to see how competitors stack up? Check out our comparison of Claude Sonnet 5 versus GPT-5.6, plus our full guide to using Claude AI.

Contents

• What is GPT-6 Astra?
• What's New in GPT-6 Astra?
  • End-to-End Computer Control
  • Generate Complete Documents, Presentations & Spreadsheets
  • Ask Smart Questions Instead of Guessing
  • Maintain Context Notes Across Long Coding Sessions in Codex
  • Perform Defensive Cybersecurity Tasks
• GPT-6 Astra Benchmark Results
  • OSWorld 2.0 and Computer Use
  • FrontierMath Tier 4 and GPQA Diamond
  • Coding: Terminal-Bench and FrontierCode
  • Security: ExploitBench and SRE-Bench
  • ARC-AGI-3 and Long Context Handling
  • Safety and Compliance Metrics
• How GPT-6 Astra Stacks Up Against Competitors
• Pricing and Access Information
• Final Thoughts

What is GPT-6 Astra?

GPT-6 Astra is OpenAI's new flagship model, replacing GPT-5.6 Sol as the top option for reasoning, computer use, and autonomous agent work.

OpenAI's positioning rests on three core capabilities:

• Most advanced computer use ever
• A breakthrough in professional work execution
• A major leap in cybersecurity capabilities, crossing OpenAI's Critical risk threshold in their Preparedness Framework

The most striking benchmark for professional users is OSWorld 2.0: Astra hits 72.6% accuracy while completing tasks in roughly 40 minutes each, versus 65.7% accuracy and 75 minutes for GPT-5.6 Sol.

Higher accuracy plus faster execution is what separates an agent you need to babysit from one you can actually trust with work. Astra launches alongside an updated Codex harness that OpenAI says completes tasks 1.9x faster than Sol's current experience on the Mind2Web benchmark.

The model is available now as gpt-6-astra through OpenAI's API and Amazon Bedrock, plus GPT-6 Astra Pro for Pro, Business, and Enterprise tiers.

Key information about GPT-6 Astra

What's New in GPT-6 Astra?

Astra's improvements focus on executing autonomous tasks: controlling computers, creating polished professional documents, staying focused during long coding sessions, and respecting safety boundaries.

Here are the standout capabilities:

End-to-End Computer Control

Astra can directly operate your computer, handling tedious multi-step work that normally eats up hours.

Think filling out batch expense forms, updating CRM records, running QA checks on a newly built website, or troubleshooting software while watching what happens on screen.

Speed in real-world conditions is what actually matters here.

In OSWorld 2.0 simulations with realistic latency, Astra achieves 72.6% success on tasks averaging 40 minutes each, versus 65.7% success in 75 minutes for Sol—that's a 47% reduction in task time per attempt.

OSWorld 2.0 measures real computer operation: navigating actual interfaces, clicking, typing, and completing multi-step tasks the way a human would.

That 47% time reduction matters as much as accuracy improvement because agent costs scale with wall-clock time. A model finishing work in 40 minutes instead of 75 doesn't just work faster—it costs roughly half as much to run for the same job volume.

Independent testing from ARC Prize shows that Astra's standout numbers on computer use and reasoning depend heavily on the testing harness being used.

On ARC-AGI-3, a standard stateless harness produces results ranging from roughly 17% to 63% depending on reasoning difficulty, while an adapter harness that maintains state hits the ~99.9% figure OpenAI published. If you call the model without state maintenance, expect substantially lower scores than the charts show.

Generate Complete Documents, Presentations & Spreadsheets

Astra is trained to produce polished professional output that actually follows your templates, not just generic drafts.

It creates documents, presentations, spreadsheets, and analyses that match your writing style and visual branding. It also filters for genuinely important context rather than dumping everything it knows into the output.

In OpenAI's own demo, Astra built a full slide deck about a fictional company based on a few sample slides, maintaining consistent tone and layout throughout.

For anyone who's lost hours reformatting Markdown output to fit corporate templates, template compliance is worth checking first.

With the Sites feature in ChatGPT, Astra can create, host, and share websites, web apps, and games from a single prompt, plus it has better image understanding than earlier models.

Ask Smart Questions Instead of Guessing

Astra decides on its own when to ask you versus when to proceed based on reasonable assumptions.

When instructions could mean several things, it handles routine details and only asks clarifying questions when the answer would change the final result.

In OpenAI's direct comparison, GPT-5.6 Sol automatically built a personal portfolio website in 13 minutes 15 seconds, while Astra paused after 20 seconds to ask which career field you were pivoting to.

Inside Codex, Astra can ask questions asynchronously: it keeps working on tasks that don't depend on your answer and only waits when a decision is actually needed.

Astra also stays focused on the original goal even when mid-task instructions change.

Earlier models sometimes treated mid-stream corrections as a completely new objective and lost the original constraints. Astra integrates new requirements and answers follow-up questions without derailing the overall work.

Maintain Context Notes Across Long Coding Sessions in Codex

GPT-6 Astra introduces a new approach that lets Codex keep and retrieve context even when the context window is full, replacing repeated summarization with searchable notes.

Previously, models used compression techniques to condense long debug sessions or major code refactoring into a single summary. This approach often lost crucial details about why a fix failed or how a component works.

With Astra, Codex maintains notes across context windows and lets you search information from earlier windows. You can find a requirement or test result from old messages even if the notes didn't explicitly record it. Enable this experimental feature in Codex's config.toml file. OpenAI says it'll become the default for Astra within weeks.

Perform Defensive Cybersecurity Tasks

Astra has crossed OpenAI's Critical risk threshold for cybersecurity per their Preparedness Framework. It's both the strongest new capability and the most restricted feature.

At launch, Astra supports code review and patch development for security, but refuses to build proof-of-concept exploits.

OpenAI plans to expand access through their Daybreak program with lighter-touch safeguards, enabling vulnerability validation, proof-of-concept development, malware analysis, and threat detection research.

Because the risk is higher, extra safety checks may interrupt or block legitimate defensive work. In ChatGPT or Codex, you might be asked to reconsider an action. On the API, a task stops immediately.

GPT-6 Astra Benchmark Results

Astra sets new records in computer use, math, coding, and cybersecurity according to OpenAI's published evaluations. Notably, this model typically uses fewer output tokens than GPT-5.6 Sol or Claude.

Scores below come from OpenAI's launch benchmark table. Treat this as vendor-supplied data and note any caveats about testing methodology.

GPT-6 Astra performance benchmark results

OSWorld 2.0 and Computer Use

Astra scores 72.6% on the OSWorld 2.0 offline dataset, versus 65.7% for GPT-5.6 Sol and 70.2% for Claude Opus 5.

OSWorld measures an agent's ability to complete real desktop tasks—navigating applications, manipulating files—so it's the most realistic measure for the question: "Will this actually do computer work for me?"

On ScreenSpot-Pro, which tests the ability to identify and interact with UI elements on screen without helper tools, Astra hits 92.7%, well ahead of Sol's 76.9% and Claude Fable 5's 87.3%.

On Agents' Last Exam, the model scores 59.3%—higher than Opus 5's 55.5% and Sol's 53.6%—while using roughly 65% fewer output tokens than Opus 5.

FrontierMath Tier 4 and GPQA Diamond

Astra scores 97.6% on FrontierMath Tier 4 v2, the hardest tier of a rigorous math benchmark. Compare that to 87.8% for both Claude Fable 5.1 and Fable 5, and 73.2% for Claude Opus 5.

OpenAI describes this as saturation, which is a fair call given the test's ceiling.

On GPQA Diamond—graduate-level questions in biology, chemistry, and physics—Astra scores 96.0%, versus 95.3% for Gemini 3.8 Flash and 94.6% for GPT-5.6 Sol.

But this model doesn't lead every benchmark. On Humanity's Last Exam (with tools allowed), Astra scores 57.2%, falling behind Claude Fable 5.1 at 65.0% and Opus 5 at 63.6%. It doesn't dominate reasoning across the board.

Coding: Terminal-Bench and FrontierCode

On Terminal-Bench 4.0—which tests agents on software engineering, system configuration, and data analysis in the command line—Astra scores 57.7%, versus 37.3% for GPT-5.6 Sol, 55.8% for Claude Fable 5.1, and 19.1% for Gemini 3.8 Flash.

That's a meaningful lead over Gemini Flash, but only a slight edge over Fable 5.1.

On other coding benchmarks, performance gaps shrink.

Astra scores 53.3% on FrontierCode 1.1 Main, matching Fable 5 at 53.5% and Opus 5 at 53.4%. On DeepSWE v1.1, it scores 74.1%, versus 73.8% for Gemini 3.8 Flash and 69.9% for Fable 5.

In our earlier Terra versus Claude Sonnet 5 comparison, Terra scored 87.4% on Terminal-Bench 2.1, which is a different version, so cross-benchmark comparisons here aren't perfectly clean.

Security: ExploitBench and SRE-Bench

Astra scores a perfect 100% on ExploitBench, compared to 78.5% for GPT-5.6 Sol and 70% for Claude Opus 5. It also hits 42.4% on ExploitGym versus Sol's 30.3%.

ExploitBench measures the ability to turn a known vulnerability into working exploit code. Because Astra maxes it out, OpenAI decided to restrict access to this capability at launch.

On a deduplicated version of ExploitBench controlled for recent data (June-August 2026) built on vulnerabilities from the previous three months, Astra scores 39.0% versus Sol's 5.5%. On SRE-Bench, which tests the ability to reverse-engineer sourceless binary files, Astra solves 88.0% on the first attempt, compared to 55.9% for Sol.

Independent lab Irregular reported that Astra solved 86 of 226 FrontierCyber challenges, versus 34 for Sol, including detection of zero-day vulnerabilities in browsers and cloud databases.

ARC-AGI-3 and Long Context Handling

Astra scores 99.9% on ARC-AGI-3 when using OpenAI's provider adapter harness, versus 7.8% for GPT-5.6 Sol and 30.2% for Claude Opus 5.

This number deserves careful reading: Independent testing by ARC Prize shows the standard stateless harness achieves scores between 17% and 63% depending on reasoning difficulty. The ~99.9% figure comes from using a stateful adapter harness with a rigorous testing process that costs tens of thousands of dollars. If you're calling the model via stateless API without state management, don't expect ~99% on this benchmark.

For long-context handling, Astra achieves 100% on OpenAI's MRCR v2 test (8-needle variant) at 256K-512K tokens and 96.3% at 512K-1M tokens, versus 91.5% and 73.8% for GPT-5.6 Sol respectively.

This reliability at 1 million tokens represents a genuine step forward for large-scale document processing compared to Sol.

Safety and Compliance Metrics

Astra scores 2.4% on OpenAI's internal safety benchmark for computer-based tasks (lower is better), versus 22.0% for GPT-5.6 Sol, 9.5% for Fable 5.1, and 11.5% for Opus 5.

In real-world environments, Astra's rate of non-compliant outputs without review is 3.4%, while Sol's is 18.8% and GPT-5.6 Terra's is 13.5%.

The one setback OpenAI flagged is monitoring chain-of-thought reasoning.

Astra produces shorter, tighter reasoning steps and controls chain-of-thought output far better than Sol. Specifically, when constrained to 750-1,250 token chain-of-thought, Astra respects limits in 60.9% of cases versus 16.1% for Sol.

The UK's AI Safety Institute found Astra can evade oversight when given adversarial instructions. OpenAI treats this as an important research priority.

How GPT-6 Astra Stacks Up Against Competitors

Here's a comparison of Astra's scores against direct competitors on key benchmarks.

Benchmark	GPT-6 Astra	GPT-5.6 Sol	Claude Fable 5.1	Claude Opus 5	Gemini 3.8 Flash
OSWorld 2.0	72.6%	65.7%	—	70.2%	—
FrontierMath Tier 4 v2	97.6%	83.0%	87.8%	73.2%	—
GPQA Diamond	96.0%	94.6%	93.7%	93.7%	95.3%
Terminal-Bench 4.0	57.7%	37.3%	55.8%	52.3%	19.1%
ExploitBench	100.0%	78.5%	—	70.0%	—
ARC-AGI-3 (adapter harness)	99.9%	7.8%	—	30.2%	—

Pricing and Access Information

GPT-6 Astra rolls out first to a limited group of organizations, then to all ChatGPT Plus, Pro, Business, and Enterprise users over the coming days, plus the OpenAI API and AWS.

Business admins can enable this for individual workspaces. It defaults to off at launch. Pro, Business, and Enterprise subscribers also get access to GPT-6 Astra Pro.

For developers, the model is available as gpt-6-astra on OpenAI's API and Amazon Bedrock.

Standard API pricing is:

• Input: $10 per million tokens
• Output: $50 per million tokens
• Fast mode: 2.5x speed at double price (~$20 input, $100 output per million tokens)
• Separate pricing for cache read/write operations

For context, this runs substantially higher than GPT-5.6 Terra's $2/$12 pricing and higher than Claude Opus 5's $5/$25 rates.

Astra is positioned as a frontier model for reasoning and automation, not a general text-processing tool.

The model supports a no-retention policy for eligible API customers. Usage counts against your current subscription tier, with options to buy additional credits.

Final Thoughts

With GPT-6 Astra, OpenAI is arguing that the next competitive frontier is autonomous task execution and direct computer interaction—not just chat quality.

The impressive math and reasoning scores are worth noting, but the number that actually matters is 72.6% success in 40 minutes on OSWorld 2.0. An agent that completes real desktop work faster and more accurately than Sol will make a concrete difference for most teams.

Temper excitement about AGI with two important caveats:

The standout ARC-AGI-3 score depends on a stateful, expensive harness system, so stateless API users shouldn't expect ~99%. Also, Astra actually falls behind Claude Fable 5.1 and Opus 5 on the Humanity's Last Exam benchmark with tools enabled.

This is a powerful, specialized model—not a one-model-fits-all system that dominates every metric.

Cybersecurity is the aspect to watch closely. Crossing that risk threshold means Astra applies strict controls—for example, refusing to build proof-of-concept exploits until Daybreak access opens up—and safeguards might pause legitimate defensive work.

If security work is part of your workflow, plan for potential interruptions and read the safety documentation carefully before deciding to deploy.

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Description: OpenAI's GPT-6 Astra sets new records in computer use and reasoning. See benchmark results, pricing, and availability details.

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Which AI Models Power Google Flow's Video and Image Generation?

Which AI Models Power Google Flow's Video and Image Generation?

On AI

Google Flow taps into multiple AI models to handle video creation, video editing, and image generation. When you're working with video creation and editing in Flow, you'll choose between Veo 3.1 and Gemini Omni Flash. For images, the platform relies on the Nano Banana family of models. Each one comes with different capabilities and limitations—some excel at text-to-video generation, others work better with image-based creation, and some specialize in extending existing footage. Understanding what each model can do will significantly speed up your workflow.

Below is the complete breakdown of the video models available on Flow and their specific features.

What Video Models Does Google Flow Include?

The video generation models on Google Flow each support different capabilities.

• How to edit video in Google Flow

Feature	Veo 3.1 - Lite	Veo 3.1 - Fast	Veo 3.1 - Quality	Gemini Omni Flash
Text-to-video conversion	Supports both 16:9 and 9:16 aspect ratios Video lengths: 4s, 6s, 8s			Both aspect ratios Video lengths: 4s, 6s, 8s, 10s
Image-to-video: Opening frame	Both aspect ratios Video lengths: 4s, 6s, 8s			Both aspect ratios Video lengths: 4s, 6s, 8s, 10s
Image-to-video: Opening frame + closing frame	Both aspect ratios Video lengths: 4s, 6s, 8s			Coming soon
Style/Reference video	Both aspect ratios 8-second videos only		Not supported	Both aspect ratios Video lengths: 4s, 6s, 8s, 10s Supports character/avatar reference and advanced audio
Video extension	Both aspect ratios 8-second videos only Veo 3.1 Lite, Fast, and Quality versions supported	Not supported	Not supported	Coming soon

Note: All 8-second Veo 3.1 videos can be extended, but you'll need to use Veo 3.1 Lite to do the extending.

2. Gemini Omni Flash Video Model

Gemini Omni Flash is now available to all users. What's interesting here is the expanded feature set compared to earlier generations. Here's what stands out:

Generate 10-second video clips

Gemini Omni Flash lets you create videos in multiple duration options:

• 4 seconds
• 6 seconds
• 8 seconds
• 10 seconds

The 10-second option gives you significantly more breathing room to showcase longer scenes or deliver a complete narrative beat within a single take.

Edit uploaded or Flow-generated videos

You can apply new prompts and input elements to modify videos:

• Videos you upload yourself
• Videos previously created with Gemini
• Specific content, styles, or elements within your video

This means you can iterate and expand on existing footage instead of starting from scratch every time.

Create custom voice profiles

Gemini Omni Flash lets you generate personalized voiceovers on demand. The basic workflow is straightforward:

• Select a base voice
• Write a prompt describing how you want to modify or style the voice
• Gemini applies your specifications to generate a customized version

You can specify preferences for:

• Tone and style
• Emotional undertones
• Delivery and pacing
• Voice characteristics

3. Image Generation Models on Google Flow

• How to create and edit images on Google Flow

When you need to generate frames or image components for your projects, Flow offers three different image creation and editing models to pick from:

Nano Banana Pro

Nano Banana Pro is the premium image model built for professional-grade work. It excels with:

• Complex design requirements
• High-precision image output
• Intricate details that demand accuracy
• Professional-level control over image generation

This model comes as the default for Google AI Ultra subscribers.

Nano Banana 2 Lite

Nano Banana 2 Lite prioritizes speed and efficiency while maintaining quality. It's optimized for:

• Fast image generation
• Quick edits
• High-quality output despite the speed
• Resource efficiency

This is the free default model and works well for standard image creation and editing tasks.

Nano Banana 2

Nano Banana 2 strikes a middle ground—fast image generation paired with solid quality output.

• Best when you need:
• Good-quality image creation
• Existing image edits
• Quick processing speeds
• A balance between speed and fidelity

Image Generation Models Comparison Table

Model	Core Function	Best For
Nano Banana Pro	Complex image creation/editing, high precision, professional control	Google AI Ultra users
Nano Banana 2 Lite	Fast image creation/editing with high quality	Free users
Nano Banana 2	Quick, high-quality image creation and editing	Standard use cases

Quick Summary: AI Image Models on Flow

• Nano Banana Pro → Premium choice for complex designs and professional workflows
• Nano Banana 2 Lite → Fast and free, perfect for basic image creation and editing needs
• Nano Banana 2 → Standard option that balances speed with quality for routine image work

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Description: Explore the AI models behind Google Flow: Veo 3.1, Gemini Omni Flash for video, and Nano Banana for images.

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Generate Cinematic Drone-Style Videos from Still Images Using Google Flow

Generate Cinematic Drone-Style Videos from Still Images Using Google Flow

On AI

Got a static image you want to transform into silky-smooth, cinematic drone footage without needing complex 3D camera setups? Google Flow makes this surprisingly simple by letting you draw guides directly on your image to control exactly how the AI should move the camera through your scene.

In this guide, we'll walk through crafting the right prompt and setting up your motion guide image so Google Flow understands the flight path and camera direction correctly. Best part? Those guide lines disappear completely from the final video—and the drone itself stays invisible.

Creating Cinematic Drone Videos on Google Flow: The Annotation Method

Step 1:

Start by preparing your image, then grab your device's drawing tool and annotate it with two key elements: a red line showing the flight path and white arrows indicating where the camera should look. Here's what makes this approach clever—the camera can move in one direction while looking in another, creating natural, intentional aerial camera movements rather than robotic, rigid motion.

Check out the example below with its red line and white arrow annotations.

Step 2:

Head over to Google Flow, switch to video generation mode, select the Omni Flash model, and configure your output duration and video count preferences.

Next, upload your annotated image and paste the detailed prompt below, then submit to generate your video.

Use the attached annotated image as the motion-control blueprint for the video.

The red line and white arrows are ONLY visual motion instructions. They are not objects, elements, or details belonging to the scene.

RED LINE = exact camera movement trajectory.

WHITE ARROWS = exact camera viewing direction.

Interpret these annotations as invisible camera-control data. Use them to determine the camera's movement and orientation, but NEVER render, reproduce, trace, animate, or preserve the annotations in the final video.

Create a single continuous aerial camera POV from a camera mounted on an unseen flying platform.

IMPORTANT: THE DRONE / AIRCRAFT ITSELF MUST NEVER BE VISIBLE.

The final video must show ONLY the camera's point of view looking into the scene. Never show the drone, aircraft, quadcopter, FPV drone, helicopter, wings, propellers, rotors, arms, landing gear, body, silhouette, reflection, shadow, or any other physical part of the flying platform.

The camera moves through the scene according to the RED LINE.

Follow the red trajectory exactly, including its starting point, direction of travel, curves, turns, waypoints, relative movement, and endpoint.

At the same time, the camera looks according to the WHITE ARROWS.

The movement direction and viewing direction are independent.

DO NOT automatically point the camera in the direction of movement.

If the red line moves in one direction while a white arrow points in another direction, follow both instructions simultaneously:

camera position follows the RED LINE

camera orientation follows the WHITE ARROW

The red trajectory has priority for camera movement.

The white arrows have priority for camera orientation.

Maintain this relationship continuously throughout the entire shot.

Use smooth, physically realistic aerial camera motion with natural inertia, gentle banking, realistic acceleration and deceleration, and subtle stabilization. The movement should feel like a real professional aerial camera, not like a computer-generated object.

One continuous shot only.

No cuts.

No transitions.

No teleportation.

No sudden jumps.

No abrupt camera swings.

No automatic reorientation to the direction of travel.


Preserve the original scene exactly as shown in the underlying image. Do not change the environment, architecture, terrain, objects, subject, lighting, colors, textures, weather, or geometry.

Allow perspective, framing, and apparent subject scale to change naturally only because the camera follows the specified red trajectory and white viewing directions.

The annotations are NOT part of the scene.

The final video must contain:

ONLY the clean original scene + the specified camera movement.

The final video must NOT contain:

red lines, white arrows, waypoints, trajectory markings, annotations, guides, overlays, graphics, text, UI elements, watermarks, or any visible drone or aircraft.

The first frame and every subsequent frame must show a clean scene with no visible production guides and no visible flying platform.

That's it—you'll have a complete drone video generated from nothing but those simple drawn lines. Here's an example of what the final result looks like:

Generating Drone Videos on Google Flow: The Prompt Method

Step 1:

Visit Google Flow using the link below, then click New Project to get started.

• Access Google Flow

Step 2:

In Google Flow's AI content creation menu, select "Create video from images".

Then upload your image to convert into video. Pro tip: choose images with minimal subjects and few small details cluttering the background—they work best.

Step 3:

Enter a command in the text field to tell Google Flow how to transform your image into drone footage.

"A cinematic drone shot zooming out very slowly with the person continuously, not stopping, running towards the drone with arms stretched wide open embracing the beauty."

Feel free to customize this prompt to match your vision. Once you're happy with it, click the arrow icon to send your request to Google Flow.

Step 4:

Wait for Google Flow to process and convert your image into video.

Your finished drone video will display smooth, artistic motion. To save it, hit the download button.

Every Google account comes with 100 free credits to create videos. Free accounts will see a Veo watermark in the bottom-right corner of generated videos.

---

Description: Learn how to transform a static photo into smooth, professional-looking aerial drone footage using Google Flow's AI and simple line annotations.

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How to Configure ChatGPT to Challenge Your Ideas (Instead of Just Agreeing With You)

How to Configure ChatGPT to Challenge Your Ideas (Instead of Just Agreeing With You)

On AI

Most people approach ChatGPT the same way: ask a question, get an answer. But here's what many miss—you can actually rewire how the AI responds to you. With just a few customization tweaks, you can transform ChatGPT from a yes-machine into a genuine thinking partner that questions your assumptions, identifies weak spots in your logic, and pushes back when something doesn't add up. This matters because the best ideas come from scrutiny, not validation.

This shift proves particularly valuable for research, writing, learning, and decision-making—anywhere you need honest feedback rather than comfortable agreement. Below is a practical guide to reconfigure ChatGPT's behavior.

Why ChatGPT Is Designed to Agree With You

ChatGPT defaults to a people-pleasing mode—it wants you to feel good about your ideas

Tính năng giọng nói của ChatGPT trên Pixel 10 Pro

Why build an AI that constantly validates you? Because it works. An AI that gently reinforces your thinking keeps you coming back. An AI that challenges you, pokes holes in your arguments, and occasionally tells you you're wrong? That feels less rewarding in the moment.

The business logic is straightforward: engagement drives usage. But here's the uncomfortable truth—that endless agreement might actually hold you back. An echo chamber feels good. It's also useless for getting better at anything.

Think about the people who've genuinely helped you improve. They weren't the ones who nodded along with everything you said. They were the ones who asked tough questions, flagged your blind spots, and weren't afraid to disagree. That's exactly what you should want from your AI assistant too.

You can absolutely reshape ChatGPT into that kind of partner. It takes one simple adjustment.

Step-by-Step: Configuring ChatGPT for Better Critical Thinking

Step 1: Access Personalization Settings

Log into ChatGPT, click on your account name in the top-right corner, then select Personalization.

Step 2: Add Custom Instructions

Scroll down to Custom Instructions and enter one of the following prompts. Pick whichever resonates most with how you work.

For balanced critical thinking:

Prioritize critical analysis over agreement. When I share an idea, opinion, or plan, examine its logic carefully. Point out untested assumptions, potential weaknesses, risks, and alternative perspectives. If I'm factually wrong or missing important context, tell me directly—explain why and provide credible evidence or reasoning. Don't try to make me feel good unnecessarily. Focus on accuracy, objectivity, and usefulness.

For more aggressive pushback:

Act as a professional skeptic. By default, stress-test my reasoning before responding. Identify logical fallacies, hidden assumptions, missing information, and what I might be overlooking. If multiple valid perspectives exist, present them instead of picking one. Only agree when there's solid grounding for it.

For research, writing, and analytical work:

Clearly distinguish between verified fact, inference, hypothesis, and opinion in your responses. If there isn't enough information to draw a conclusion, say so instead of guessing. When appropriate, state your confidence level and identify what additional information would strengthen the analysis.

For maximum rigor (comprehensive version):

I want you to prioritize accuracy over agreement with me. Treat my ideas and conclusions as hypotheses to test, not as defaults to accept. Actively challenge them—highlight weaknesses, untested assumptions, logical errors, or factual gaps. Suggest alternative angles when relevant. If I'm wrong, say so clearly and explain why, backed by credible reasoning or evidence. Always distinguish between fact, inference, and opinion. If we lack enough information to conclude something, be direct about that. Keep your tone frank, objective, and respectful while helping me think better rather than just confirming what I want to hear.

Pick the version that matches your needs and paste it in.

Now ChatGPT will actually engage with what you're saying. It'll still keep the conversation constructive, but it won't shy away from questioning your reasoning, identifying gaps, and being honest when evidence is thin.

Click Save at the bottom to lock in your settings.

Step 3: Test It Out

Try pitching an idea you're confident about and watch the difference.

Example prompt: "I think AI will completely replace teachers within 10 years. Once AI can teach lessons, grade papers, and answer any question, schools won't need human teachers anymore."

Instead of just elaborating on your point, ChatGPT will now examine the logic, separate fact from assumption, flag unproven claims, warn when conclusions overreach, and suggest more precise framing.

You've now turned ChatGPT into a thinking partner who stress-tests your ideas instead of just validating them.

The Difference It Makes: Before and After

Your Prompt	ChatGPT Default Response	With Custom Instructions
I'm thinking about launching a paid newsletter about productivity hacks. I don't have an existing audience yet, but productivity is a huge market, so I think there's still room for a new newsletter. Is this a good idea? (Max 3 sentences)	It could work, but generic productivity tips are probably too broad to build a paid newsletter from zero. Start with a defined audience and specific problem (e.g., productivity for overwhelmed managers, ADHD-friendly systems, or productivity for founders), build a free audience first, then charge. The opportunity is less "productivity is huge" and more "I can be uniquely valuable to a specific group."	This idea is weak. "Generic productivity tips" is saturated and lacks differentiation—you have no distribution advantage. A large market doesn't equal easy opportunity; it actually demands a sharp niche, unique perspective, or specific audience willing to pay. Don't move forward until you can explain in one sentence why someone should pay for your content instead of reading thousands of free alternatives.
My boss offered me more responsibility but no raise or promotion. I'm thinking of accepting because it shows my value and might open better opportunities later. Is this smart? (Max 3 sentences)	Yes—if taking on more responsibility gets you visibility, skill development, or momentum toward advancement, it could be a strategic move. But don't accept added work without clarifying expectations and asking what specific results will lead to a raise or promotion. Get it in writing with timelines and criteria to avoid "we'll discuss it later" becoming indefinite.	Not automatically. Taking on more work without a clear agreement on compensation, scope, or advancement roadmap might just prove you'll do more for the same pay. Clarify what concrete benefits you'll receive for delivering results and when that decision gets made—before you commit.
I'm planning an article titled "7 Ways Modern Smartphones Are Better Than Before." It'll cover things like better cameras, faster processors, improved battery life, and brighter screens. Is this concept compelling? (Max 3 sentences)	Yes—it's strong, accessible, with clear improvements readers instantly grasp. To strengthen it, frame these seven points around real benefits rather than just listing upgraded specs.	The angle is too generic. Saying "phones now have better cameras, faster chips, and brighter screens" is obvious—it reads like a spec sheet, not an insight. A stronger approach: focus on what modern improvements have quietly eliminated that annoyed users on older phones.

See the pattern? The custom instructions don't make ChatGPT hostile. They make it honest. There's a real difference, and it compounds over time.

---

Description: Learn how to customize ChatGPT's responses to provide critical feedback and rigorous analysis instead of passive agreement.

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Master ElevenLabs v3: Creating Emotionally Expressive AI Voices

Master ElevenLabs v3: Creating Emotionally Expressive AI Voices

On AI

ElevenLabs v3 represents a significant leap forward in text-to-speech technology. This latest generation focuses heavily on expression, emotion, and producing speech that genuinely sounds human. What's interesting here is that v3 moves beyond simply converting text into audio—it gives you real control over how sentences are delivered.

You can now shape your AI voice to convey joy, sadness, anger, and countless subtle emotional nuances. The question is: how do you actually harness these capabilities to create natural, emotionally resonant audio? Let's break down the entire process for configuring ElevenLabs to produce genuinely expressive speech.

Step-by-Step: Configuring ElevenLabs v3 for Expressive Output

Step 1: Access the Platform and Prepare Your Text

Start by logging into your ElevenLabs account and navigating to the Text to Speech section.

Once the interface loads, paste your text into the input field, then select your desired voice from the available options.

Step 2: Choose Your Language and Filter Voices

First, select the language for your narration. This helps ElevenLabs identify voices that match your content's linguistic context.

The platform will display multiple voice options in your selected language. To narrow things down, use the category tags below to refine your search.

ElevenLabs offers extensive filtering options to help you locate the perfect voice for your specific needs.

Step 3: Activate v3 and Generate Enhanced Audio

When you've found your preferred voice, click Use to select it. Next, select the ElevenLabs v3 model to activate the enhanced capabilities.

Below your text, you'll notice the Enhance button, which optimizes voice quality using the v3 engine.

ElevenLabs will add pronunciation markers to your text automatically. Now hit Generate to process your audio file.

Step 4: Compare and Download Your Results

You'll now see two versions of your voiceover—both enhanced for naturalness, emotion, and authenticity. Listen to each one and download whichever version resonates with you.

Below are two audio examples processed through ElevenLabs v3. Notice how the vocal delivery feels more natural and emotionally present—much closer to how a human would actually speak.

---

Description: Learn how to fine-tune ElevenLabs v3 for natural, emotionally rich text-to-speech output. Step-by-step guide.

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Setting Up CLAUDE.md for Claude Code: Complete Guide with Templates

Setting Up CLAUDE.md for Claude Code: Complete Guide with Templates

On AI

A well-crafted CLAUDE.md file won't do your thinking for you—but it eliminates the tedious repetition of explaining the same technical constraints, boundaries, and verification processes in every conversation. This is where the real productivity gains happen.

The file works best when it's not just a generic wish list. Instead, it should tell Claude Code which commands actually work in your setup, where the critical code lives, which files are off-limits for changes, and how to verify when a task is genuinely complete.

Table of Contents

• What CLAUDE.md Does
• Hierarchical Structure: System-Level, Project-Level, and Subdirectories
• What Goes Into Each File
  • System-Wide CLAUDE.md
  • Project-Level CLAUDE.md
  • CLAUDE.local.md
  • Rules for Subdirectories
• System-Wide CLAUDE.md for Your Workflow
• A Lean Template for Your Project Root
• How CLAUDE.md Differs from Auto Memory
• What If Claude Code Ignores Your Rules?
• Working Alongside Other Development Tools

What CLAUDE.md Does

CLAUDE.md is a standard Markdown file that Claude Code loads into its project memory and uses as supplemental guidance during work sessions. It's the ideal place to store details that apply across multiple tasks:

• Reliable commands for development, testing, and building your application
• An overview of key directories and main entry points
• Architectural patterns and dependency conventions
• Hard boundaries—for example, "never run database migrations without explicit approval"
• Clear completion criteria so Claude can verify the work actually meets requirements

This doesn't replace a detailed task description. Specific outputs, scope, and acceptance criteria should still be stated in your current prompt. Conversations can be trimmed or lose important context over time. Put long-term project rules and constraints in this file instead of relying on chat history.

Hierarchical Structure: System-Level, Project-Level, and Subdirectories

Claude Code assembles configuration files based on your working path. On startup, it layers content from outermost folders inward. CLAUDE.local.md in the same directory gets loaded after CLAUDE.md. A CLAUDE.md in a subdirectory gets included in context when Claude reads files in that area.

Example of a real-world hierarchy

~/.claude/CLAUDE.md
  Personal rules applied to every project

~/projects/shop/CLAUDE.md
  General shop project guidelines

~/projects/shop/apps/admin/CLAUDE.md
  Specific rules for the admin frontend

~/projects/shop/apps/admin/src/payments/CLAUDE.md
  Extra safeguards for payment processing

Put shared project information in CLAUDE.md at your project root, or in .claude/CLAUDE.md one level up. Pick a convention and stick with it across the repository. Write rules so they don't conflict with each other—Claude Code merges file contents instead of applying a priority system to resolve contradictions.

Claude Code's official documentation on memory hierarchy

Anthropic covers project-level, user-level, and local memory files in detail in the official Memory documentation. The exact interfaces and terminology may shift, so check back there when making major configuration changes.

What Goes Into Each File

System-Wide CLAUDE.md

Your ~/.claude/CLAUDE.md file holds personal rules that apply across multiple projects. This might include formatting preferences, your preferred languages for code and comments, or your personal authentication workflow. It shouldn't contain assumptions tied to any specific repository.

Project-Level CLAUDE.md

This file lives in your repository and describes the overall project state. Keep it under version control when your team needs to stay aligned on common commands and rules. Only include information that actually reflects your codebase, configuration, or decisions the team has agreed on.

CLAUDE.local.md

Use CLAUDE.local.md for personal additions at the same location—like local test data or your personal workflow shortcuts. Add this to .gitignore. Passwords, API keys, customer data, and other sensitive information have no place in any CLAUDE.md file.

Rules for Subdirectories

Creating files in subdirectories makes sense when a specific area has unique risks or conventions. Payment processing, data migrations, and mobile apps are classic examples. Don't duplicate your entire root file here—just add rules specific to that area.

Pro Tip: An effective rule is one that drives specific decisions. "Write clean code" is vague. "Run existing migration tests before altering database schema" is something you can actually verify.

System-Wide CLAUDE.md for Your Workflow

Your system-level file at ~/.claude/CLAUDE.md should be repository-agnostic. This is where you define how Claude works with you in general. Avoid assumptions about specific frameworks or projects.

Sample content for ~/.claude/CLAUDE.md

# Personal Working Guidelines

## Collaboration
- Explain your plan and verification steps before making large changes.
- Ask clarifying questions if expected outcomes or scope aren't clear.

## Code
- Prefer TypeScript if the project already uses it.
- Use the project's existing formatters and test runners.

## Before You Finish
- List changed files and completed checks.
- Flag any remaining risks or unknowns.

A Lean Template for Your Project Root

Create the file directly in your project root, or let Claude Code draft an initial version with /init. Auto-generated drafts won't capture your project's nuances. Review, remove bad assumptions, and only add rules you actually follow.

CLAUDE.md template for projects

# Project Name

One sentence describing the product and who uses it.

## Key Directories
- src/app/: routes and pages
- src/lib/: business logic and integrations
- tests/: automated test suites

## Running and Verifying
bash
npm run lint
npm run test
npm run build


## Core Rules
- Check for existing components before creating new ones.
- Validate all input at API boundaries.
- Document changes to public interfaces.

## Hard Limits
- Never log sensitive information to files or output.
- Don't run data migrations without a confirmed plan.
- Ask before proceeding if requirements are unclear.

## Task Complete When
- All agreed-upon checks pass.
- Documentation and tests reflect actual behavior.

Use the @imports syntax to reference long, stable documents. A line like @docs/architecture.md will load that file at startup. What's interesting here is that imports only support a limited depth, so avoid importing your entire documentation suite by default.

How CLAUDE.md Differs from Auto Memory

Auto Memory stores reusable insights privately and locally. The system discovers stable project patterns as Claude works and learns from them. It's different from guidelines you've vetted and shared in your repository.

CLAUDE.md remains the authoritative source for standards, security boundaries, commands, and architectural decisions. Think of Auto Memory as a helper tool. The /memory command lets you view and manage stored items.

What If Claude Code Ignores Your Rules?

1. Run pwd to confirm Claude Code launched in the intended project directory.
2. Open /context and check what's loaded in the current session.
3. Use /memory to make sure Auto Memory isn't conflicting with a specific project rule.
4. Run claude --version and claude doctor if setup or configuration looks unusual.
5. Try claude --safe-mode if you're troubleshooting customization errors. This mode skips CLAUDE.md files, skills, plugins, hooks, MCP servers, and Auto Memory. Authentication, model selection, built-in tools, and permissions still work normally. Management policies remain active.

If the file loaded but Claude still misbehaved, narrow down your rules and name specific verification steps. Instead of "be careful with emails," write "display the recipient, subject, and body for confirmation before sending."

Working Alongside Other Development Tools

Some teams also use AGENTS.md files. Avoid duplicating rules across multiple files when you can. Claude Code can include a shared file through an import like @AGENTS.md. Then your CLAUDE.md (kept concise) can explain why that file takes the lead and list Claude-specific additions.

Review your actual commands and most critical rules after each major change. A short, accurate file beats a lengthy collection of outdated guidelines every time.

---

Description: Learn how to create effective CLAUDE.md files at system, project, and subdirectory levels to guide Claude Code through your workflows.

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Converting Stories Into Storyboards With Dola AI

Converting Stories Into Storyboards With Dola AI

On AI

Turning a story or script into a comic book traditionally means breaking it down into individual scenes and determining what content belongs in each one. Here's the thing: doing this manually eats up serious time. You're reading, analyzing, reorganizing plot points, and basically doing all the heavy lifting yourself.

Dola offers a smarter approach. The platform can automatically convert your story content into a detailed comic storyboard script in text format. It handles the scene sequencing for you, then suggests settings, characters, actions, and dialogue for each panel. Essentially, it gives you a solid foundation to develop into a full comic or series of illustrations.

Creating a Storyboard Script on Dola AI

Step 1:

Head to Dola AI using the link below and log into your account. In the chat interface, click Quick to switch to Pro mode.

https://www.dola.com

Step 2:

Now select the Skills section to access available tools.

When the skills menu appears, click on Create Comic Storyboard to get started.

Step 3:

Enter your conversion request—describe the story or idea you want transformed into a specific number of scenes. Here's an example prompt to follow.

Convert the following content into a vibrant, cheerful, and colorful animated storyboard with 8 scenes. For each scene, describe the setting, characters, actions, expressions, camera angle, and dialogue in Vietnamese. Keep character details and visual elements consistent across all scenes.

Content: Lan is a 17-year-old girl living with her grandmother in a small, colorful house. One afternoon while tidying the storage room, Lan discovers a glowing star-shaped key that sparkles mysteriously. Curious, she carries the key and explores the house. She happily finds a small staircase leading down to a basement she never noticed before. There, she spots a secret door with a star-shaped lock. When Lan inserts the key, the door swings open and brilliant light floods out. Behind it is a hidden room filled with old photographs, toys, and precious family keepsakes. Lan excitedly explores the room and decides to tell her grandmother about this discovery. Finally, Lan and her grandmother look through the old memories together and happily share family stories.

Step 4:

Wait a moment for Dola to process your request and generate the storyboard. You'll receive complete scene content and scripts—in this case, eight fully developed scenes.

Review the output and request refinements if needed. Want more scenes? Change the dialogue? Add character details or environmental descriptions? You can iterate until it matches your vision.

Step 5:

Once you're satisfied, use this storyboard as your foundation. You can build image-generation prompts from each scene or use it as a blueprint for developing your full comic. Click the save icon to export the storyboard content as a CSV file.

Tips for Creating Better Storyboards on Dola AI

To get results that actually match what you're envisioning, provide a story with clear progression. Be specific about scene count, visual style, and what details should appear in each scene. If your story has multiple characters, describe their distinctive features so the scenes stay visually consistent. Here's the basic formula for your prompt:

[Conversion Request] + [Scene Count] + [Style] + [Content Details] + [Language]

Example: Convert this story into 8 comic scenes + cheerful animation style + describe setting, characters, actions, and dialogue + write in Vietnamese.

• Scene count: 6, 8, 10, or whatever works for your story.
• Language: specify if you want output in Vietnamese or another language.
• Style: animated, manga, fairytale, comedic, realistic—whatever fits your vision.
• Scene information: setting, characters, actions, expressions, dialogue.
• Consistency: request that character traits and visual details remain constant across scenes.
• Detail level: if you'll use this to generate images later, ask for more specific descriptions of settings and actions.

---

Description: Learn how to transform narrative ideas into detailed comic storyboards using Dola AI's automated scene-building tool.

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Transform Rough Sketches Into Polished Images With ChatGPT's New Sketch Tool

Transform Rough Sketches Into Polished Images With ChatGPT's New Sketch Tool

On AI

Here's something genuinely cool: ChatGPT's latest feature, called Sketch, lets you turn rough doodles into AI-polished artwork without typing a single prompt. Just draw something—anything, really—and ChatGPT transforms your scribbles into a finished image that matches your vision.

What makes this stand out is the simplicity. You don't need to write detailed commands or explain your concept in text. Every step happens through ChatGPT's interface. Feed it a rough sketch, and the AI analyzes it, then generates a complete, detailed image you'd never create by hand alone. It takes your rough concept and turns it into something visually polished and easy to understand. Below, we'll walk you through using ChatGPT Sketch on both mobile and desktop.

Creating Finished Images From Sketches on ChatGPT Sketch Mobile

Step 1:

Open ChatGPT and tap the menu icon to access the options. Navigate to the Images section, then select the Sketch tool.

Step 2:

The Sketch interface loads and you're ready to draw directly into ChatGPT. You can also switch to block mode if you prefer drawing with shapes instead of freehand lines.

Step 3:

Tap the colored circle to pick your stroke color. Then draw whatever you want and hit send. ChatGPT Sketch immediately analyzes your sketch and converts it into a complete, refined image.

The image generation prompt is already built in, so you don't need to modify anything.

Step 4:

Wait a moment, and your finished image appears.

If you like the result, download it. Or add new text commands to refine or adjust the image further.

Using ChatGPT Sketch on Desktop

The desktop version gives you more control—you can actually edit the AI prompt if needed.

Step 1:

Go to Images and select Sketch to begin.

Next, choose your stroke color.

Step 2:

Pick your drawing style—either block shapes or simple line sketches.

One desktop-exclusive feature: you can add text directly to your sketch.

Step 3:

Once you're happy with your sketch, hit send. You can even combine multiple sketches together. After that, you'll see the complete image generation prompt, and you're free to edit it if you want. Finally, send it again and let ChatGPT generate the final result.

What's interesting here is: you end up with a polished, complete image built from just two messy doodles. If any part doesn't quite match your vision, you can circle areas and request refinements.

---

Description: ChatGPT Sketch turns your messy doodles into professional artwork. No prompts needed—just draw and let AI do the magic.

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