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This is HUGE.

OpenAI just shocked the entire coding world with the new GPT-5.6.

It actually dominated Claude Fable 5 in some really key areas, wow.

GPT-5.6 outshines both Claude Fable and the legendary Claude Mythos in multiple high-profile benchmarks.

Unbelievable scores in the most challenging benchmarks out there.

GPT-5.6 beats out almost every model convincingly in the AI Code Arena Frontend leaderboard, scoring near Joint 1st, with Claude Fable 5

And it’s not just way more intelligent — it also comes with an entire new model family, and multiple new first-party tools to build incredible things with the power of GPT-5.6.

Including a brilliant new competitor to OpenClaw.

This model is so powerful that OpenAI actually had to spend weeks convincing the US government that it was safe enough to release into the wild.

1. A brand new model family

GPT-5.6 outclasses its predecessor in frontend web design:

They’ve completely abandoned their traditional one-model strategy.

GPT-5.6 now comes in three permanent capability tiers that can evolve independently over time.

Sol is the flagship model for advanced coding, research, cybersecurity, and complex agent workflows.

Terra is the balanced everyday workhorse, combining strong reasoning with lower latency.

Luna is highly optimized for speed and cost — making it ideal for high-volume tasks like customer support, translation, and summarization.

Instead of paying flagship prices for every request, we can now choose the right model for each workload.

2. Record-breaking leaps in intelligence and coding ability

For the first time in several months, an OpenAI model tops the highly granular DesignArena frontend benchmarks, with the new GPT-5.6:

GPT-5.6 Sol is now OpenAI’s most capable model.

It sets new state-of-the-art results across several major benchmarks, including TerminalBench, BrowseComp, and Humanity’s Last Exam.

On TerminalBench 2.1, which evaluates real-world software engineering tasks, Sol scored 88.8%, rising to 91.9% in its new Ultra reasoning mode.

On BrowseComp, it achieved 92.2%, while Humanity’s Last Exam climbed to 52.7%, outperforming GPT-5.5 as well as Claude Fable 5 and Claude Opus 4.8. OpenAI also says Sol reaches these scores while using significantly fewer output tokens than previous models.

Perhaps the most impressive breakthrough comes from ARC-AGI-3, a benchmark designed to measure how well AI adapts to completely unfamiliar problems.

Sol became the first frontier model to solve a public ARC-AGI-3 task.

The interesting part isn’t just that it performs better. It’s how it reasons.

When one approach fails, Sol dynamically forms a new hypothesis instead of repeatedly executing the same flawed plan — a major weakness of previous-generation AI agents.

3. So good at hacking it delayed the launch

GPT-5.6 outclasses top Claude models in the extremely long-horizon Agent’s Last Exam benchmarks, achieving the best results for an extreme fraction of the cost:

GPT-5.6’s cybersecurity capabilities became advanced enough to trigger an additional U.S. government national security review before public deployment.

The model shows major improvements in command-line operations, vulnerability discovery, threat modeling, exploit analysis, code review, and automated patch generation.

Those gains required an equally large investment in safety. According to OpenAI, Sol now blocks roughly 10× more potentially malicious cyber activity than previous generations while remaining below the company’s “Critical” capability threshold.

4. Massive efficiency gains = massive cost savings

GPT-5.6 demonstrates an incredible ability to generate complex, highly sophisticated interactive diagrams and visualizations from scratch:

GPT-5.6 isn’t just smarter — it’s substantially more efficient.

Sol is 54% more token-efficient on AI coding tasks than previous reasoning models while still achieving better results. It delivers top-tier performance using less computation, reducing both latency and API costs.

And the pricing reflects that efficiency too.

Sol costs $5 per million input tokens and $30 per million output tokens, while Terra and Luna give you progressively cheaper alternatives for less demanding workloads.

5. ChatGPT Work and multi-agent orchestration

ChatGPT Work using multiple tools and features to get achieve tasks blazingly fast:

GPT-5.6 also powers ChatGPT Work, OpenAI’s new platform built around delegation and getting things done.

Its headline feature is Ultra mode, which can coordinate up to four AI agents simultaneously across parallel workstreams.

Instead of solving a project sequentially, GPT-5.6 splits it into multiple tasks, lets separate agents tackle them in parallel, and combines the results into a finished deliverable. That pushes TerminalBench performance from 88.8% to 91.9%.

ChatGPT Work also integrates directly with tools like Slack, Google Drive, Microsoft Teams, CRM platforms, and local desktop files.

This lets GPT-5.6 spend hours building web apps, updating spreadsheets, generating reports, or completing other multi-step workflows with minimal supervision.

GPT-5.6 is going to seriously revolutionize the AI coding landscape.

With stronger reasoning, industry-leading coding performance, permanent capability tiers, major efficiency gains, and true multi-agent orchestration, it’s one of OpenAI’s biggest releases in years.

This is huge.

Claude Sonnet 5 is seriously revolutionary.

A Claude model that matches up to the most powerful and intelligent models out there — yet several times lighter, cheaper, and faster.

It literally dominated every single non-Claude model in several reputable benchmarks — and shockingly matched up to Opus 4.8.

Only Fable 5 was significantly better in the Arena AI leaderboard.

The normal, non-mini version GPT-5.5 could only beat out Sonnet 5 at its absolute maximum thinking effort (xhigh) on the Artificial Analysis Leaderboard:

And it’s not just about the massively improved intelligence density or efficiency.

Sonnet 5 also fixes a major issue that many developers have faced when dealing with Claude models.

The self-improving and autonomous ability is on another level.

All this with ultra-competitive pricing that will give us massive savings in token costs.

1. Intelligence autonomous multi-step execution and self-verification

This is one of the biggest upgrades in Sonnet 5.

It’s now so much better at carrying out extended sequences of work without constant supervision.

By leaping more than 13 percentage points to hit 80.4% on Terminal-Bench 2.1, Claude Sonnet 5 represents the largest terminal-based engineering jump in the lineup’s history, completely closing the gap with the flagship Opus 4.8

Previous Claude models were excellent at planning — but they still struggled to reliably follow through on complicated workflows involving debugging, testing, and iterative refinement. Sonnet 5 is build from the ground up to stay on track.

The new and improved self-verification is on another level.

It now verifies its own work from top to bottom before returning it.

Instead of generating a fix and hoping it’s correct, the model can reproduce a bug, implement a solution, test the fix, temporarily remove it to confirm the issue returns, and then restore the working version — all without being explicitly instructed to perform each step.

Proactively fixing problems and edge cases you would never have considered.

2. One million token context — but somehow even better

Sonnet 5 also uses an incredible 1 million token context window — making it practical for us to work across enormous codebases, lengthy documents, and large enterprise knowledge repositories.

But here’s what makes it such a big deal now — Anthropic has completely removed the pricing penalty that previously applied to very large prompts.

Earlier Sonnet models charged premium rates beyond 200,000 tokens — but Sonnet 5 offers standard pricing across the entire one-million-token context window, making large-context applications far more economical.

3. Massive improvements in agentic coding capabilities

Anthropic has also made enormous progress in agentic coding.

Claude Sonnet 5 narrows the gap to flagship-level intelligence by surging to a 63.2% pass rate on SWE-bench Pro—marking a major 5.1 percentage point leap over Sonnet 4.6 and landing within striking distance of Opus 4.8’s field-leading 69.2%.

Compared with both Claude Sonnet 4.6 and even the more expensive Claude Opus 4.8, Sonnet 5 is significantly better at handling extended software engineering workflows with minimal human intervention.

It can maintain context across long coding sessions, make coordinated changes across multiple files, recover from mistakes — all while rapidly progressing toward the larger objective.

A super-powered collaborative engineer capable of managing the most substantial development tasks.

4. Adaptive thinking by default

One of the most interesting changes happens before the model even begins generating a response.

Rather than immediately producing an answer — Sonnet 5 now uses adaptive thinking by default.

It automatically decides how much reasoning a problem requires, spending more time on difficult coding and analytical tasks while responding quickly to simpler requests.

We can also control this behavior, adjusting the reasoning effort from Low to Extra High depending on whether they prioritize speed or accuracy.

At its highest settings, Sonnet 5 reaches reasoning and coding performance comparable to Claude Opus while maintaining the efficiency expected from the Sonnet family.

5. Built-in real-time cybersecurity guardrails

Claude models have been getting scary good at finding vulnerabilities in codebases that we once thought were highly secure.

So Anthropic has taken extra precaution to prevent bad actors from being able to use it to exploit vulnerabilities in the most critical software systems.

Claude Sonnet 5 is the first Sonnet-tier model to include real-time cybersecurity protections by default.

It’s specifically designed to refuse requests related to active exploit development, network compromise, and other offensive cybersecurity activities, bringing its safety profile closer to Anthropic’s highest-tier models.

Claude Sonnet 5 is all about making AI much more useful and practical in our everyday workflow as developers.

Its biggest advances lie in how it works: executing long workflows reliably, verifying its own output, reasoning more deeply when needed, handling vastly larger contexts without extra cost, and operating with stronger built-in safeguards.

Claude Skills have had such a huge impact on the entire AI coding ecosystem.

Yet many developers still choose to ignore them and just keep prompting — unaware of the massive transformative effects this could have on their workflow.

Skills have been as game-changing as the Model Context Protocol (MCP) — and these two form a deadly combo when put together.

MCP lets your agents do real things, Skills teach them how to do those thing — in a much more sophisticated way than normal prompting.

Instead of stuffing thousands of lines of instructions or coding standards into every conversation, Skills package instructions, workflows, scripts, and supporting resources into reusable modules that Claude loads only when they’re relevant.

That makes them more context-efficient, more reliable, and far more capable than traditional prompt engineering.

Here are 5 incredible things that make Skills so essential in every serious developer’s daily workflow.

1. Intelligent progressive disclosure

Claude Skills uses a clever three-level loading system to choose the exact skill to deploy at the right moment.

• Level 1: Claude always loads a tiny YAML metadata header describing each skill.
• Level 2: If your request matches that metadata, Claude loads the skill’s SKILL.md instructions.
• Level 3: Large reference docs, examples, and edge-case documentation stay in separate files that Claude only opens if a workflow explicitly requires them.

Context window bloat has always been one of the biggest limitations of LLMs.

If you preload every workflow into every conversation, you quickly waste tokens and dilute the model’s attention.

Instead of carrying every playbook into every prompt, Claude expands only the knowledge needed for the current task.

2. Skills can run in multiple invocation modes

Claude Skills gives you two different invocation modes.

Passive Context runs automatically in the background. For example, a TypeScript Conventions skill can ensure Claude never uses any, always prefers interfaces, and follows your team’s coding standards whenever it writes code.

Direct Actions are explicitly invoked workflows. Commands like /commit or /review-pr trigger structured, repeatable processes without requiring you to rewrite the same instructions every time.

This gives you both invisible guardrails and reusable automation.

3. They’re more than prompts — they execute code

Traditional prompts rely entirely on probabilistic reasoning.

Claude Skills can also include executable Python, Bash, or other local scripts, allowing them to combine AI reasoning with deterministic execution.

For example, a skill can generate code, run a compiler or linter, validate a database schema, execute tests, and feed those verified results back into Claude before continuing.

Instead of guessing whether something works, the workflow can prove it.

4. Skills can learn from your codebase

Advanced skills often maintain a persistent learnings.md file.

As Claude encounters unusual bugs, failed approaches, or project-specific edge cases, it records what it learned. Future executions can read those notes before solving similar problems.

Rather than remaining static instructions, Skills gradually adapt to the realities of your codebase over time.

5. Skills can work together

Skills aren’t limited to acting in isolation.

A complex request might trigger a Research Skill, which hands its findings to an Architecture Skill, followed by a Testing Skill that generates unit and integration tests.

Some advanced workflows can even spawn sandboxed subagents to perform independent tasks — like gathering database metrics via MCP — before reporting the results back to the primary workflow.

Instead of one giant prompt trying to do everything, specialized skills collaborate to solve increasingly complex engineering tasks.

Claude Code Skills symbolized a major transition from prompt engineering to capability engineering.

Progressive loading keeps context efficient, passive and direct invocation make workflows flexible, executable scripts provide deterministic reliability, persistent learnings help skills evolve with your project, and composable skills enable sophisticated agentic workflows.

The result isn’t just a smarter prompt — it’s an intelligent, reusable system that continuously expands what Claude can do.

Things just got even wilder with this incredible AI design tool.

Google just made it 10 times more powerful than it already was — it’s just terrifying now what it can do.

We are no longer just talking about turning one or two text prompts and sketches into UI mockups and front-end code.

The old Google Stitch: pretty awesome, but still too short-sighted and primitive:

Now Google Stitch wants to totally eradicate web designers taking over the entire process of designing an app — from idea-start to code-finish.

The new Google Stitch: full-fledged design engineer:

The fact that it literally now has its own MCP servers to integrate with Claude Code and the rest tells you everything you need to know…

The focus is now on the entire design system, not just one or two cool screens.

• How it evolves and all the different directions it could take
• How cohesive and well-defined the design language is
• How seamlessly the design transfers to the live codebase

1) AI-native infinite canvas for multimodal design

The upgraded Stitch introduces a redesigned interface built around an infinite canvas where users can combine text, screenshots, sketches, references, and even code in one space.

Instead of relying on a single prompt, the canvas becomes the working context for the design agent.

You can:

• Drop UI inspiration images directly onto the canvas
• Add product requirements or notes beside layouts
• Paste existing components or code snippets
• Generate multiple UI directions side-by-side
• Iterate visually instead of sequentially

This turns Stitch into a visual thinking environment where ideas, references, and outputs live together.

2) Project-aware design agent

Stitch now includes a design agent that understands everything on the canvas and uses it as context for generating interfaces. The agent can interpret requirements, follow style direction, and evolve designs as the project grows.

Key capabilities:

• Generate full app flows from high-level descriptions
• Expand a single screen into a multi-screen product
• Modify layouts based on new instructions
• Maintain visual consistency across generated pages
• Create alternate design directions instantly

The agent works continuously with the canvas rather than responding to isolated prompts.

3) DESIGN.md for reusable design systems

A major addition is DESIGN.md, a structured file that stores design rules, branding, layout preferences, and component behavior. Stitch uses this file as a persistent source of truth when generating UI.

With DESIGN.md you can:

• Define typography, spacing, and color tokens
• Enforce brand consistency across screens
• Share design systems between projects
• Import design rules from external sources
• Export system logic for developers

This allows Stitch to generate interfaces that follow consistent design language automatically.

4) Instant interactive prototyping

Stitch can now transform generated layouts into working interactive prototypes. Instead of static screens, designs can simulate navigation, flows, and user interactions.

Capabilities include:

• Clickable navigation between generated screens
• Auto-generated user journeys
• Multi-screen flow simulation
• Interactive preview mode
• Logic-based next screen generation

This allows teams to validate product flows immediately after generating UI.

5) Voice-driven design and live critique

The upgrade introduces voice interaction directly inside Stitch. Users can speak instructions, request feedback, and iterate designs conversationally.

Examples:

• Ask Stitch to redesign a landing page verbally
• Request alternative layouts using voice
• Get live critique of UX decisions
• Ask the agent to improve hierarchy or spacing
• Iterate rapidly without typing

This makes the design workflow more fluid and conversational.

6) Higher-quality UI generation with improved model capabilities

The latest version improves layout reasoning, spacing, hierarchy, and multi-screen coherence. Stitch can now generate more structured and realistic interfaces across different product types.

Enhancements include:

• Better responsive layout structure
• Improved component consistency
• Stronger visual hierarchy
• More realistic product UI patterns
• Cleaner spacing and typography

These improvements make generated designs closer to production-ready outputs.

7) MCP server support for connected workflows

The upgrade also introduces MCP (Model Context Protocol) server support, allowing Stitch to connect to external tools, environments, and development workflows.

With MCP support, Stitch can:

• Connect to component libraries
• Access external design systems
• Interface with developer environments
• Pull context from connected tools
• Push generated UI into implementation workflows

This allows Stitch to function as part of a larger AI-powered product development pipeline rather than a standalone design tool.

Stitch at launch

• Prompt or image in
• UI screens out
• Chat-based refinement
• Theme adjustments
• Export to Figma or front-end code

Stitch after the recent major upgrade

• Infinite canvas for text, images, and code
• Persistent project-aware design agent
• Agent manager for parallel explorations
• DESIGN.md for reusable design rules
• Interactive prototyping and flow generation
• Voice-based critique and live edits
• MCP server integration for connected workflows
• Improved generation quality with newer models

That comparison shows the real story: Stitch has evolved from a fast UI generator into a more opinionated AI design environment.

The new Stitch is designed for a wider audience than traditional design tools usually target. It works for both professional designers exploring many variations and founders shaping a first product idea.

The practical implication is that Stitch now sits at an interesting intersection:

• for non-designers, it lowers the barrier to making presentable interfaces
• for designers, it speeds up ideation and branching
• for developers, it tightens the handoff from design intent to code and downstream tools

The strongest part of the upgrade is that these pieces reinforce each other. The infinite canvas creates richer context, the design agent uses that context, DESIGN.md stabilizes consistency, prototypes make ideas testable sooner, voice interaction reduces friction, and MCP integration connects everything to real development workflows.

Anthropic just shocked the world — they just released the most powerful AI model in human history.

This is Claude Fable 5 — the public-facing version of the monstrous Claude Mythos model that they’re hiding from the public.

There is no contest — this thing completely decimated all the benchmarks out there — Opus 4.8 looks like a toy compared to what this thing can do:

• Dominated several AI model leaderboards within hours of launch
• Migrated a major tech company’s codebase of 50 million lines in less a day — previously took several months of work
• Better than any other model at coding & software engineering

It’s been absolutely wild — People are casually one-shotting entire games, 3D worlds, full-stack apps, and code optimizations that shouldn’t be possible, but here we are.

And to think that this is actually a restricted version?

Imagine having a model so powerful that you literally cant release it to the world without disastrous consequences.

1. The Fable vs. Mythos story: A tale of two twins

It’s definitely one of the most fascinating aspects of this incredible launch.

The Claude Fable 5 and Claude Mythos 5 relationship.

Both models share the same underlying architecture and core capabilities.

The main difference isn’t intelligence — it’s access.

Claude Fable 5

• Released to the general public
• Full reasoning, coding, and agentic capabilities
• Protected by real-time safety classifiers
• Automatically restricted in high-risk domains

Claude Mythos 5

• The unrestricted, god-mode version of the same model
• Available ONLY to vetted organizations
• Accessed through Anthropic’s Project Glasswing program
• Designed for advanced cybersecurity and research applications

Anthropic itself has emphasized that the naming distinction reflects safety boundaries rather than capability differences.

In other words, Fable isn’t weaker model. It’s a safer one.

A pair of identical twins — one allowed to walk freely among the public.

The other considered so dangerous that its access is limited to people responsible for defending critical digital infrastructure.

That alone makes this launch unlike anything we’ve seen before in AI.

2. Absolutely decimates benchmarks

Fable 5’s benchmark results are among the most impressive ever reported for a public AI model.

On Humanity’s Last Exam, one of the toughest AI reasoning tests in existence, Fable scored 64.5% with tools, comfortably outperforming GPT-5.5 (41.4%) and Gemini 3.1 Pro (44.4%).

Its dominance extends to software engineering. On SWE-Bench Pro, the industry’s leading coding benchmark, Fable achieved 80.3%, compared to GPT-5.5’s measly 58.6%.

The model also posted leading scores across computer use, spatial reasoning, cybersecurity, and terminal-based tasks, establishing itself as one of the strongest general-purpose AI systems ever released.

3. Unbelievable long-horizon coding ability

Fable 5 is specially built for massive, complicated tasks that unfold over hours — or even days.

The model can:

• Explore its environment independently
• Build and execute its own plans
• Launch parallel sub-agents
• Monitor its own progress
• Self-correct when things go wrong

The most shocking example comes from Stripe.

The Stripe test

Fable was tasked with working inside a 50-million-line Ruby codebase and completed a codebase-wide migration in a single day—compressing what would normally require months of engineering effort into hours.

It’s just absolutely shocking how crazy things have gotten.

But this is what Anthropic means by long-horizon intelligence: an AI capable of staying focused on complex objectives for hours, not just seconds.

4. Sophisticated real-time Opus hand-off

One of Fable’s most innovative features is something users may never notice.

When the model encounters sensitive areas such as:

• Advanced cybersecurity
• Biochemical research
• Model distillation
• Other high-risk domains

…it doesn’t simply refuse.

Instead, Anthropic’s safety systems automatically hand the conversation off to Claude Opus 4.8, which completes the task under stricter safety constraints.

According to Anthropic:

• Roughly 95% of user sessions run entirely on Fable 5
• Only a small fraction trigger the Opus fallback
• Users receive a seamless experience rather than a hard refusal

It’s a novel approach to AI safety: switch models instead of shutting the conversation down.

5. Why the main Mythos simply had to be locked up

If it’s the same model, why not release it publicly?

Cybersecurity.

Serious cybersecurity concerns.

Anthropic has unambiguously described Mythos 5 as “possessing unprecedented offensive and defensive cyber capabilities”.

Its performance on security-focused benchmarks significantly exceeds previous generations — Mythos-class systems have reportedly helped identify thousands of critical vulnerabilities.

It even uncovered a 27-year-old security flaw in OpenBSD and wrote a remote code execution exploit for a 17-year-old bug in FreeBSD completely on its own.

It is so effective at finding bugs that open-source maintainers literally begged Anthropic to slow down its disclosures because human developers couldn’t write the patches fast enough.

For Anthropic, the risk-reward equation was clear:

• Release a safeguarded version to the public
• Restrict the unrestricted version to trusted partners
• Maintain oversight of the model’s most powerful capabilities

This model is going to completely rip apart everything we thought was possible with AI models and AI-powered software development.

This is just unbelievable.

Imagine having a model that’s:

• More than 5 times cheaper than Claude Opus — yet just as intelligent?
• Blazing fast — up to 15x faster than Claude Opus for heavy prompts and massive context?
• Open-source with open weights to top it all off?

Yeah it’s definitely no surprise how so many people have been going absolutely wild over the few days over the new MiniMax M3.

Look how closely it matches Claude’s and GPT’s capabilities! It’s even better in certain key areas:

I still can’t wrap my head around the absolutely wild feats of endurance and intelligent it displayed during testing — and has been performing ever since then.

This just truly completely shattered the limits of what we thought was possible with open-source models — or any type of model really.

1. Not the same “1 million token context”…

Don’t let AI companies fool you with their loud claims of 1 million token context.

Not all 1 million token contexts are remotely the same — not even close.

Yes they can all process massive codebases, research archives, books, or long-running projects.

But they vastly differ in the quality of processing all that massive data, how much they can actually make sense of, how much they comprehend the interconnectedness of all the data.

And speed. A big, big one.

Most AI models become painfully slow and expensive when dealing with that much information.

M3 uses an innovative new approach called MiniMax Sparse Attention (MSA) that allows it to focus on the important parts instead of wasting resources on everything at once.

This allows M3 to:

• Read huge amounts of information more than 9x faster
• Generate responses more than 15x faster
• Use a fraction of the computing power normally required

The giant context window is no longer just for fancy — it actually becomes a beast in the real world.

And those models that still use slower methods of parsing large token contexts will get absolutely left in the dust.

2. Insane long-horizon ability

MiniMax M3 shocked every with insane feats of autonomy on tasks spanning several hours.

The 12-hour research paper loop: M3 was given a complex AI research paper and spent 12 hours straight reproducing the core experiments. It managed its own context window, parsed charts, wrote code, handled 18 GitHub commits, and generated 23 experimental figures completely unaided.

Self-training models: In another test, M3 was given raw base models and spent 12 hours handling data synthesis, training, and evaluation loops on them entirely on its own.

The 24-hour CUDA optimization: It was assigned to optimize an FP8 GEMM kernel (a notoriously painful low-level GPU operation). Over 24 hours, it autonomously went through 147 benchmark submissions and nearly 2,000 tool calls, boosting hardware peak utilization from a lousy 7.6% to a highly optimized 71.3% — a 9.4x speedup with zero human help.

This is the kind of work you’d normally assign to a high-level engineer or researcher, yet here comes MiniMax M3 doing it all on its own, unbelievable.

3. Punching way above its weight

Despite competing against much larger companies with way bigger resources at their disposal, M3 is posting amazing benchmark results.

• SWE-Bench Pro: 59.0%, edging past GPT-5.5 and Gemini 3.1 Pro
• BrowseComp: 83.5, outperforming Claude Opus 4.7’s 79.3

MiniMax is competing right at the cutting edge, particularly in software engineering and autonomous research tasks.

4. Ultra-competitive pricing

It will undoubtedly be one of the biggest selling points.

All that intelligence and speed and innovation, for such a ridiculously low price.

M3 launched at roughly:

• $0.60 per million input tokens
• $2.40 per million output tokens

That makes it significantly cheaper than many of the most advanced AI models available today.

Just compare to the latest Claude Opus pricing:

• $5.00 per million input tokens
• $25.00 per million output tokens

This will be huge for startups and smaller teams.

Many AI projects fail not because the technology isn’t good enough, but because running them becomes too expensive. Lower costs mean more companies can build products that were previously out of reach.

5. Open weights available to everyone

MiniMax isn’t keeping everything locked behind a proprietary wall.

And open-source availability means:

• Greater transparency
• Community-driven improvements
• Easier experimentation
• More deployment flexibility
• Reduced vendor lock-in

Open models used to trail the very best closed systems — not anymore.

M3 is part of the new wave of the models that completely disrupts the notion of open-source models being inherently weaker.

Why M3 matters

The biggest takeaway from M3 isn’t that it’s smarter than every other model.

It’s that MiniMax is attacking some of the biggest problems in AI at the same time:

• Cost
• Speed
• Long-term autonomy
• Multimodal understanding
• Accessibility through open source

If these early results hold up in real-world use, M3 could become one of the most important AI launches of the year — not because it’s the biggest model, but because it makes powerful AI far more practical for everyone.

Claude Opus 4.8 is absolutely insane.

This is going to fundamentally transform the AI coding ecosystem and the sheer amount of tasks we entrust agents with. You absolutely cannot ignore this.

Claude Opus 4.8 stays perfectly on track across extremely long complex coding sessions:

Like it’s not just way more powerful than Opus 4.7 with so many abilities massively upgraded —

It’s also a much more HONEST model:

It can actually admit when it has no idea what something means — instead of making up nonsense on the fly.

It’s literally FOUR TIMES more likely to catch any potential bugs and flaws in any code it generates.

All thanks to a major breakthrough in AI self-awareness and reliability.

And don’t get me started on the new Dynamic Workflows that turn any Claude-powered agent into a super-powered beast.

It even easily passed the tricky car wash test that’s frustrated so many great models — which shows a serious jump in real-world understanding and logic — something very important for AGI.

1. A massive leap in AI integrity & reliability

One of the biggest problems with modern AI is that models often don’t know when they’re wrong.

They write code with bugs, reach conclusions on incomplete information, and confidently report success even when they’ve failed.

Opus 4.8 was designed specifically to fix this.

The model proactively identifies gaps in its own reasoning instead of rushing toward conclusions…

The model is far more likely to identify uncertainty, flag gaps in its reasoning, and challenge questionable assumptions before proceeding.

• Admitting uncertainty: The model proactively identifies gaps in its own reasoning instead of rushing toward conclusions.
• Catching bugs: Opus 4.8 is 4× less likely than Opus 4.7 to let flaws in code it generated pass without comment.
• Reporting failures honestly: The model achieved a perfect 0% rate of uncritically reporting flawed results in Anthropic’s internal evaluations.
• Pushing back when necessary: Rather than blindly following instructions into failure, it actively questions weak assumptions and incomplete plans.

As AI systems become increasingly autonomous, reliability starts to matter more than raw intelligence.

A model that catches its own mistakes can end up being much more valuable than one that’s simply smarter.

2. Dynamic Workflows change everything

The second major announcement is Dynamic Workflows.

Instead of tackling large projects as a single agent, Opus 4.8 can act as an orchestrator, spawning and managing hundreds of parallel AI subagents simultaneously.

With Dynamic Workflows, Claude can:

• Write orchestration scripts
• Launch hundreds of specialized AI agents
• Distribute work across large codebases
• Verify outputs against existing test suites
• Aggregate results into a final solution

Anthropic showcased this capability by using Dynamic Workflows to help port the Bun runtime from Zig to Rust — a project involving roughly 750,000 lines of code.

Using a swarm of coordinated agents, Claude completed the migration in 11 days and achieved a remarkable 99.8% pass rate on Bun’s existing test suite.

That’s not a chatbot helping write functions.

That’s an AI system coordinating work at the scale of an engineering organization.

3. Completely ridiculous intelligence gains

Unbelievable gains in practically every major benchmark.

But the most eye-catching result comes from USAMO 2026 — one of the world’s most difficult mathematics competitions.

Opus 4.7 scored 69.3.

Opus 4.8 scored 96.7.

That’s NOT normal.

But it didn’t stop there.

• USAMO 2026: 69.3 → 96.7
• SWE-Bench Pro: 64.3 → 69.2
• GPQA Diamond: 84.1 → 88.4
• Humanity’s Last Exam: 35.8 → 44.7
• AIME 2025: 79.4 → 88.9
• GDPval: 1753 → 1890

The pattern is clear: this wasn’t a model that got better at one thing — it got better at almost everything:

• Reasoning
• Coding
• Mathematics
• Scientific problem-solving
• Knowledge work
• Agentic tasks

Virtually every category moved forward — and many of the jumps are unusually large by frontier-model standards.

4. You can now control how hard Claude thinks

Opus 4.8 also introduces manual effort controls across Claude.ai, Claude Code, and the API.

Users can now decide how much compute the model should spend on a problem:

• Low effort for speed
• Medium effort for balanced performance
• High effort for difficult reasoning and coding tasks

This gives users direct control over one of AI’s most important tradeoffs: speed versus intelligence.

5. Fast Mode is faster — and cheaper

Anthropic also upgraded Fast Mode.

The new mode delivers:

• 2.5× faster token generation
• 3× lower cost than previous Claude fast modes

That matters because many AI applications today aren’t limited by intelligence—they’re limited by latency and cost.

Reducing both simultaneously makes entirely new categories of AI products economically viable.

Claude Opus 4.8 isn’t just more intelligent. It’s much more trustworthy now.

The benchmark gains are impressive. Dynamic Workflows is exciting. Fast Mode and effort controls are useful.

But the most important thing Anthropic may have accomplished is teaching a frontier model to recognize when it might be wrong.

As AI starts automating more and more crucial portions of our workflows, this starts to matter more than any benchmark score.

This is something completely different —

Qwen 3.7 Max is going to make such a massive impact on the workflow of so many developers:

This wasn’t launched with the usual displays of fancy benchmarks and claims of better reasoning — even though there’s a LOT to be said about that — almost as good as top Claude models, yet so much cheaper and faster.

Even better than the new Gemini 3.5 — and GPT-5.5 highest thinking mode!

But Qwen 3.7 Max was made specially to address one of the biggest “holy-grail” challenges facing AI agents today:

How do you keep an AI focused for hours without it losing track of its objective?

And from what I’ve been seeing these past few days — it’s been a game-changing success.

1. The shocking 35-hour test

The most impressive thing about Qwen 3.7 Max isn’t a leaderboard score:

It’s endurance.

One of the biggest weaknesses of modern AI systems is context drift—the tendency to lose track of instructions, goals, or prior decisions during long workflows.

Alibaba tested this directly by assigning Qwen 3.7 Max a highly specialized engineering challenge: optimizing a GPU attention kernel it had never seen before.

Then they left it running.

Over roughly 35 consecutive hours, the model:

• Made 1,158 tool calls
• Ran 432 code evaluations
• Diagnosed its own compilation failures
• Tested multiple optimization strategies
• Achieved a reported 10× performance improvement

This Max model maintained coherence and direction throughout the entire process—a critical capability for autonomous software engineering agents.

2. New 1 million token context window

Qwen 3.7 Max also now supports a 1 million token context window to support this incredible long-running ability.

That allows developers to work with:

• Entire code repositories
• Large documentation libraries
• Research archives
• Long project histories
• Multi-day agent memory

in a single context.

The benefit isn’t just memory. It means less summarization, less context compression, and greater continuity across complex workflows.

3. A smart ecosystem strategy

One of the most underrated features is that Qwen 3.7 Max natively supports the Anthropic API protocol.

This means developers can potentially plug it into existing tools such as:

• Claude Code
• OpenClaw
• Anthropic-based agent frameworks
• Internal Claude-compatible enterprise tooling

without rebuilding their entire stack.

Instead of forcing developers into a new ecosystem, Alibaba is reducing switching costs and making adoption easier.

4. Fast, capable, and cheap

Reasoning models are typically slow.

Qwen 3.7 Max reportedly outputs at around 192 tokens per second, significantly faster than many reasoning-focused models that operate closer to 60–70 tokens per second.

Speed matters because agents make hundreds or thousands of decisions. Small latency improvements compound into major productivity gains.

The model also ranks among the top tier of reasoning systems on independent leaderboards while delivering strong performance on difficult benchmarks such as GPQA Diamond and advanced math evaluations.

Perhaps most disruptive is the pricing:

• $2.50 per million input tokens
• $7.50 per million output tokens
• Native prompt caching that can reduce repeated-context costs by up to 90%

For companies building AI agents, that dramatically improves the economics of long-running workflows.

Many AI coding discussions are still focused on the raw intelligence of the models.

But the next wave of competition is about:

• Autonomous software engineering
• Long-horizon planning
• Tool use
• Workflow execution
• Agent reliability

So Qwen 3.7 Max isn’t just another model release, it’s a superior platform for AI agents.

Its defining characteristics aren’t just intelligence—they’re endurance, context, speed, compatibility, and cost efficiency.

And moving forward, these things are going to be far more important than another benchmark victory.

Google just released Antigravity CLI — an upgraded version of Gemini CLI that’s so much more powerful at software development.

Packed with amazing new features and ecosystem integrations, several times faster than Claude Code, and comes with brand new commands for various essential software development tasks.

The new Antigravity CLI in action:

They completely rebuilt the terminal experience around the new way software engineering works in 2026 — multi-agent, asynchronous, persistent, and increasingly autonomous.

From a regular chatbot sitting in a terminal — to a orchestration system for advanced engineering workflows.

1. Dynamic asynchronous subagents change everything

Antigravity CLI uses sophisticated divide-and-conquer — to radically improve performance and code quality for the most complicated tasks.

Main agent delegating a sub-agent to a task in Antigravity CLI:

Many coding assistants still work like linear chatbots. You ask for a change and wait while one agent handles everything sequentially.

Antigravity CLI changes that completely.

The orchestrator automatically breaks large goals into smaller tasks, spawns specialized subagents dynamically, and executes them simultaneously in the background.

You move from working with one assistant to directing an engineering team.

The context advantage is huge too.

Each subagent operates inside its own isolated context window, keeping compile logs, installs, test output, and intermediate work away from the orchestrator context.

This massively improve larger workflows for us developers.

Imagine upgrading frameworks while simultaneously migrating APIs, updating components, generating tests, and validating documentation.

Large refactors stop feeling like events.

2. Built for pure speed with Gemini 3.5 Flash

Gemini 3.5 Flash is much faster than other top-tier models:

Google optimized Antigravity CLI around Gemini 3.5 Flash, with benchmark figures placing output speed around 289 tokens per second — as much as 4 times faster than major competing tools like Claude Code.

That matters because latency compounds in agent systems.

A slow model creates slow planning, slow subagents, slow testing, slow validation, and slower iteration loops.

Everything stacks.

And Google really appears to have optimized specifically for fast orchestration.

This makes multi-file edits, codebase analysis, documentation generation, and architecture migrations feel dramatically faster.

The shorter the loop becomes, the easier iteration becomes.

And iteration drives quality.

3. Persistent isolated local environments

Antigravity CLI also introduces persistent isolated environments.

Instead of constantly rebuilding state every interaction like before, Antigravity agents can preserve files, execution state, and progress across sessions.

This enables workflows like:

• Long-running builds
• Persistent testing environments
• Multi-stage debugging
• Extended implementation sessions

For developers working on larger systems, this could remove huge amounts of setup overhead.

Authentication migrations, distributed debugging sessions, or long validation workflows no longer need constant reconstruction.

Work continues.

State survives.

4. Brand new slash commands

The command system reveals how Google expects developers to use Antigravity.

Not chat-first —

Workflow-first.

/goal tells the system to autonomously execute large objectives without repeatedly asking permission.

Think:

“Upgrade React version.”

“Refactor auth.”

“Migrate architecture.”

Then there is /grill-me, which forces the agent to interrogate you before implementation begins.

It asks about edge cases, requirements, logic gaps, and architecture decisions.

That could prevent a surprising number of specification failures.

Finally /browser gives the agent browser access for testing interfaces and validating outputs.

Frontend workflows start looking much closer to autonomous QA.

5. Deep integration with Antigravity 2.0 and AI Studio

Antigravity CLI shares the same underlying agent harness as Antigravity 2.0 desktop and Google AI Studio.

Desktop, terminal, SDK, and mobile workflows are becoming interfaces over the same runtime.

Imagine walking somewhere, opening AI Studio, speaking through a feature idea, exporting it, then sitting down later and continuing instantly inside agy.

Same state.

Same memory.

Same agents.

Ideas move directly into execution.

6. Live voice transcription inside the terminal

Antigravity CLI embeds Gemini audio models directly into terminal workflows.

You can literally ramble:

“Build dashboard… PostgreSQL… auth… analytics…”

The system transcribes and refines everything into executable objectives automatically.

This could become surprisingly useful during planning and architecture work.

A lot of design thinking happens faster through speech than typing.

Antigravity turns that directly into execution.

And that may be the bigger story.

Less prompting — more directing.

Wow this is amazing.

Google just unleashed a major new competitor to OpenClaw — Gemini Spark is going to totally transform how so many people approach their goals from now on.

And always-on AI system — always operating continuously in the background.

Seamlessly connects to every single one of your tools to work on any of your goals.

Intelligently adapts to your workflows and style and powered by the most advanced AI models on the planet.

1. Always on: set and forget

One of Spark’s biggest selling points is the always-on background execution.

Most AI products today still behave like sessions (one of the main reasons why OpenClaw blew up so much).

You ask → It responds → The interaction ends

Spark changes that model by allowing workflows to continue running after the conversation is over. Google positions it as an agent capable of monitoring, organizing, and acting in the background instead of waiting for repeated prompts.

That means less time restarting the same processes every day.

You can literally schedule anything at any time you want.

Think about workflows like:

• Monitoring emails and surfacing important updates
• Tracking ongoing projects and deadlines
• Watching dashboards or reports for changes
• Running recurring research and summaries

The value here isn’t raw speed.

It’s continuity — working on complex tasks endlessly, so you can get to more important things.

2. Teachable reusable Skills

Another major idea behind Spark is teachable Skills.

Instead of rebuilding workflows repeatedly, Spark is designed to retain context and adapt over time.

Repeated instructions → Reusable workflows

Over time, systems can begin reflecting how users naturally operate:

• Preferred writing style and tone
• Reporting structures
• Research methods
• Team workflows
• Content frameworks

The goal is simple:

Teach once.

Reuse repeatedly.

As more behavior becomes reusable, the distance between intention and execution keeps shrinking.

3. Native connections instead of screen scraping

Many AI agents today still rely on browser automation and analyzing screen pixels.

They:

• Observe screens
• Click buttons
• Mimic user actions

Spark moves toward native integrations instead:

• Deep Workspace integration
  • Gmail
  • Docs
  • Sheets
  • Calendar
• Third-party integrations through MCP (Model Context Protocol)

This allows workflows to operate closer to the application layer itself.

Instead of manually moving information between tools, Spark can coordinate across connected systems more directly.

The result:

Less tool switching. A more widely capable agent.

4. Control still sits with the user

Persistent AI naturally raises another question:

How much autonomy is too much?

Google is trying approach Spark with strong controls and approval layers.

The idea is not unrestricted automation.

It is controlled execution.

Spark can:

✅ Monitor
✅ Organize
✅ Draft
✅ Prepare workflows

While keeping human oversight where needed.

That balance matters because future agent systems will be judged not only by capability, but by trust.

5. Multi-agent coordination

Perhaps the most interesting part of Spark is its move toward multi-agent coordination.

Instead of one assistant handling everything, Spark points toward specialized systems working together.

Potential roles could include:

• Research agents
• Scheduling agents
• Communication agents
• Documentation agents
• Monitoring agents

Different responsibilities.

Shared objective.

The user gradually shifts from operator to coordinator.

And that changes the relationship between humans and AI entirely.

Gemini Spark represents a larger transition happening across AI:

• Sessions → Persistent workflows
• Prompts → Systems
• Single assistants → Agent coordination
• Manual execution → Continuous operation

Will be very interesting to see how all this evolves.