Tari Ibaba

This is completely crazy — how can a model be this good?

OpenAI is sending shockwaves across the entire AI ecosystem with the new GPT-6 Astra.

The things it’s doing are really scary.

It has destroyed so many benchmarks now — rendered them obsolete — the benchmarks were not even good enough to properly measure how intelligent it is.

So many major models dropped in the past few days and this is by far the best.

It’s already the best coding model in the world.

And not just coding but so many other things like 3d graphics and world generation.

1. This is basically AGI

This was just shocking.

An unbelievable 99.9% on ARC-AGI-3 — a benchmark tests whether an AI can face a new problem and figure out how it works.

Compare to a measly 7.8% from GPT-5.6 Sol — and 30.2% from Opus 5.

Even human testers couldn’t compare — the average human tester scored around 48%.

And once again we see another occurrence of a really important AI trend.

Astra didn’t get this huge jump from intelligence aloneOpenAI also gave it a much better state-preserving harness.

The harness helps Astra keep track of what it has learned as it works through a long task. It doesn’t have to keep starting over or rebuild its understanding from old context.

The model is getting smarter. But the system around the model is also getting much better.

2. It’s already a coding monster

Astra is also ridiculously good at coding.

On Terminal-Bench 4.0 it scored 57.9%. Claude Fable 5.1 scored 55.8% while GPT-5.6 Sol managed just 37.3%.

Astra also did it cheaper.

Its estimated API cost per task was around 9% lower than Sol and 63% lower than Fable 5.1 at the tested settings.

Then there’s DeepSWE v1.1.

This benchmark tests difficult software engineering work inside real codebases.

Astra scored 74.1%. Sol scored 72.7% while Fable 5.1 scored 67.4%.

3. Much better instruction following

Astra is also better at knowing when it needs more information.

If your instructions aren’t clear it is more likely to ask a question instead of guessing.

That sounds small — until you give an AI control of your computer.

4. Massive upgrade in computer use

This might be Astra’s most important improvement.

On OSWorld 2.0 Astra scored 72.6% and took around 40 minutes per task.

Compare to Sol which scored 65.7% and took around 75 minutes.

So Astra spent about 47% less time per task while getting more tasks right.

That’s a huge jump.

Astra also scored 92.7% on ScreenSpot-Pro. This benchmark tests whether an AI can find the right buttons and other elements inside screenshots of professional software.

Then there’s Agents’ Last Exam.

It tests AI agents on real professional work like financial modeling. It also covers engineering and media production.

Astra scored 59.3%.

Claude Opus 5 scored 55.5%. Sol scored 53.6%.

Astra also used around 65% fewer output tokens than Opus 5 at their highest-scoring settings.

AI isn’t just getting better at answering questions anymore.

It’s getting better at doing the work.

5. Astra is scarily good at hacking

This is where things get a little scary.

On ExploitBench Astra scored a perfect 100%.

The benchmark tests whether an AI can turn known software vulnerabilities into working exploits.

Sol scored 78.5%.

Astra got every one.

Its cyber skills are now so strong that OpenAI classifies Astra as its first broadly released model to reach its “Critical” cybersecurity capability level.

But here’s the interesting part.

OpenAI also ran an ExploitGym honeypot test.

The researchers placed vulnerable targets outside the agent’s assigned task. They then checked whether the AI would attack them to reach its goal.

Sol exploited those honeypots in 48.2% of relevant runs.

Astra did it 0% of the time.

So Astra is much better at hacking — but it also seems much better at knowing what it shouldn’t hack.

That’s a very important combination.

Overall this is definitely going to be one of the most transformative models of 2026.

This is HUGE.

The new Claude Fable 5.1 is absolutely insane.

It absolutely dominated every single model on the AI Arena Leaderboard — ranking an undisputed #1 👇

The gap is so massive, wow — Anthropic just unlocked a new standard of model intelligence.

And it’s much cheaper than Fable 5 — yet much more intelligent — just look at the massive difference on the leaderboard.

It found deep hidden bugs in codebases that humans couldn’t find for several years.

And it comes with an even more powerful sibling model.

1. Exceptional long-horizon coding and debugging ability

Fable 5.1 is amazing at working on hard problems for hours without losing focus.

It’s exceptional at long-horizon tasks — jobs that involve many steps and tools and can take hours to finish.

And some of the early tests are wild.

In one case Fable 5.1 reportedly found a rare software bug that engineers had struggled to fix for almost five years. It traced the problem back to an external vendor library.

In another test Ramp gave Fable 5.1 a machine learning problem and left it running for 38 hours.

The model reviewed its results and spotted a problem with its earlier work. It changed direction and launched six experiments overnight — then came back with its findings and next steps.

2. Much cheaper and more intelligent than Fable 5

There’s one obvious problem with an AI agent working for 38 hours…

It can get expensive.

Fable 5.1 keeps its base API price at $10 per million input tokens and $50 per million output tokens.

But Anthropic cut prompt cache read costs by 75% — from $1 to just $0.25 per million tokens.

This matters for AI agents because they often reuse the same information. That could include source code or instructions and project documents.

Instead of paying full price to process that information again and again the agent can use cached context.

Anthropic says this can cut typical Fable 5.1 costs by around 25%. For heavily agentic workloads the savings can reach about 45%.

So Anthropic isn’t just making agents that can work longer — it’s making them cheaper to run.

3. Much better at finding security bugs

Anthropic also reworked Fable 5.1’s cybersecurity safeguards — cutting false positives by 60%.

That means security engineers should hit fewer unnecessary refusals when scanning code for vulnerabilities.

Fable 5.1 can now find security flaws directly in source code — including potential zero-day vulnerabilities.

But Anthropic still limits higher-risk tasks like turning those flaws into usable exploits.

4. Granular adaptive thinking

Fable 5.1 also gives developers more control over how much effort the model puts into a problem.

It uses adaptive thinking.

Developers can change its reasoning effort during a conversation without breaking the prompt cache.

That’s useful because not every part of a long task needs the same amount of thinking.

Finding a strange bug might need deep reasoning. Changing a simple configuration file probably doesn’t.

So developers can use more compute when Claude hits something difficult — then dial it back for easier work.

That gives them more control over speed and cost.

5. Fable 5.1 has an even more powerful sibling

Anthropic also launched Claude Mythos 5.1 — a sibling model.

Anthropic says Fable 5.1 and Mythos 5.1 use the same underlying model. What changes is the safety layer around them.

Fable 5.1 is the version most people can use.

Mythos 5.1 targets vetted researchers who work in sensitive areas like advanced cybersecurity and life sciences. Those researchers can use capabilities that Anthropic restricts more heavily in Fable.

It’s an interesting idea — the intelligence stays largely the same while the safety boundaries change depending on who uses it.

Claude Fable 5.1 is hands-down going to be one of the most game-changing releases of the year.

Developers who ignore this are seriously wasting a lot of Claude Code’s potential.

/code-review – an incredibly underrated command.

This gem will totally transform the quality and correctness of your codebases — it will find and fix problems in places you never even imagined.

Long before they even think of showing up in production.

Problems that go way beyond the lines you changed — it goes way beyond just reviewing diffs and tell you what it thinks.

With code-review Claude can coordinate multiple AI reviewers, understand your entire repository, automatically fix issues, publish findings to GitHub, and even dynamically adapt its review style to your team’s standards.

How it works

Claude Code’s /code-review command in action

/code-review analyzes your current local Git diff (staged or unstaged changes) against your main branch.

It uses multi-agent orchestration behind the scenes, looking at your code through multiple specialized lenses (security, logic, performance, and error handling, etc.)

Let’s look at five of the most exciting features.

1. Extremely flexible review intensity

Claude Code lets you control how deeply it reviews your code by specifying an effort level.

The effort level can be one of low, medium, high, xhigh, max, and ultra

Most of the effort levels lead to a standard review with a single analysis of your changes.

But running /code-review ultra takes things to a whole new level.

Instead of relying on one reasoning process, Claude orchestrates multiple specialized review agents in parallel. One may focus on security, another on edge cases, another on architectural consistency, while others look for regressions or runtime issues.

Claude then combines their independent findings into one report, giving you broader coverage than a single review pass.

2. Understands everything single thing

Traditional review tools are mostly diff-aware — they inspect only the code you’ve changed.

Claude reviews those changes in the context of your entire repository.

For example, if you modify a function signature in one file, Claude can trace where that function is used elsewhere and detect broken call sites, regressions, or hidden dependencies—even if those files weren’t part of the Git diff.

It can also surface pre-existing issues that your new changes expose, helping you catch bugs that might otherwise remain hidden.

3. Fixes everything automatically

code-review goes way beyond just identifying problems.

Adding the –fix flag:

creates an automated review-and-repair loop.

After identifying issues, Claude reopens the affected files and writes fixes directly into your local working tree. Instead of just pointing out bugs, it proposes actual code changes that you can inspect before committing.

A major time-saver for routine fixes and minor optimizations that seriously adds up over time.

4. Publish GitHub review comments from your terminal

Claude Code integrates directly with GitHub, so you don’t have to copy review results into your pull request manually.

With GitHub commenting enabled, Claude can publish its findings as native inline PR comments on the exact lines where issues occur.

That means you can review locally, send feedback to GitHub, and stay in your terminal without constantly switching between your editor and browser.

5. Customize reviews with REVIEW.md

Every team has different review priorities.

Claude lets you encode those priorities in a repository-level REVIEW.md file.

For example, you can require every new API endpoint to include an integration test, treat missing authentication checks as critical issues, or limit the number of style suggestions so reviews stay focused on high-value feedback.

Instead of generic recommendations, Claude reviews your code according to rules that match your team’s engineering standards.

Claude Code’s /code-review is much more than an AI-powered linter.

With multi-agent reviews, automatic fixes, repository-wide analysis, GitHub integration, and customizable review rules, it acts more like an AI review platform than a simple assistant.

As AI-generated code becomes the norm, tools that can review, validate, and even remediate that code will likely become just as essential as the coding assistants that wrote it.

This new Qwen 3.8 Flash Next model is an incredible positive sign of where the AI race is heading.

The model seriously punches above its weight in so many ways.

It’s beating Claude models that cost so much more to train and have several hundreds of billions more in parameter size.

Can you believe it — this model only activates like 6 billion parameters at a time — yet it’s matching up to all those massive closed-source models with trillions of parameters.

Barely beat by Claude Fable and GPT-5.6 Sol:

Qwen3.8-Flash-Next scores 56 on Artificial Analysis’ Intelligence Index, far above the median score of 29 for open-weight models of a similar size.

This is what’s so incredible about it — it’s not the absolute smartest model out there — but it’s hands down one of the most efficient.

We saw this with GLM-5.3 too — companies are no longer so obsessed with absolute intelligence and parameter size — they are now competing in radical efficiency and innovative training techniques.

And it’s just a new model or typical update — it’s actually a stepping stone for the future generation.

Qwen 3.8 Flash Next is actually an experimental preview of the upcoming revolutionary Qwen 4 model.

And Alibaba (the makers) refused to follow the same old formula of training a bigger model and spending more and more on compute.

They didn’t focus on parameters — instead they redesigned several fundamental parts of the model from the ground up to drastically improve the efficiency.

Qwen3.8-Flash-Next’s 62.5% score on SWE-bench Pro, compared with Claude Opus 4.6 Max’s score of 53.4%, demonstrates the model’s highly competitive performance on complex, real-world software engineering tasks

And what you get is a model with frontier-level benchmark performance that can run entirely on local hardware and costs dramatically less to train than its predecessor.

You can run it entirely locally

Qwen3.8-Flash-Next is also open-weight, meaning you can download and run quantized versions without relying on a cloud API.

Community MLX builds are already running it on Apple Silicon.

Current conventional 4-bit builds generally require around 100GB of unified memory — but you can run it on much less powerful devices by increasing the quantization.

Apple’s new M5 Ultra Mac Studio is particularly powerful for running models locally. You can configure it with as much as 512GB of unified memory and 1.2TB/s of memory bandwidth.

So with enough memory you can run Qwen3.8-Flash-Next completely locally: no per-token API fees and no private documents or prompts leaving your computer.

It cost roughly one-ninth as much to train

Alibaba says training Qwen3.8-Flash-Next needed only around one-ninth of the training cost of Qwen3.7-Plus — even with the newer model outperforming its predecessor across several important areas.

That efficiency comes from a radically different architecture.

The model’s main network contains 125 billion parameters, accompanied by roughly 51 billion parameters of N-gram embeddings.

But it only activates around 6 billion parameters per token.

That’s possible because Qwen3.8-Flash-Next uses a sparse Mixture-of-Experts (MoE) architecture. Instead of running the entire network for every token, it dynamically routes each token through only the experts required for that particular computation.

The result is enormous overall model capacity without paying the computational cost of activating everything simultaneously.

Completely rebuilt architecture

MoE isn’t the only major change.

Qwen3.8-Flash-Next introduces fundamental changes across attention, residual connections, embeddings and optimization.

Its hybrid attention architecture combines Gated DeltaNet, a form of linear attention, with a new mechanism called Qwen Sparse Attention (QSA).

Rather than repeatedly examining everything inside an enormous context window, QSA uses a lightweight indexer to locate relevant information.

That helps the model maintain high inference speeds and relatively low memory requirements across extremely long prompts. It natively supports 262,144 tokens, extendable to 1 million tokens using YaRN.

Alibaba has also introduced Gated Residual, which expands the residual stream into four dynamically controlled branches, and billions of N-gram embedding parameters that increase capacity without requiring equivalent active computation.

It all comes down to one thing:

More intelligence without proportionally more compute.

This is really a preview of Qwen 4

And this might be the most important part.

Alibaba calls Qwen3.8-Flash-Next an early preview of the architecture that will power Qwen4.

So as a developer you get to test these ideas before the full Qwen4 family arrives.

The direction is pretty clear.

For years AI companies mostly chased better models by adding more parameters and more compute.

Qwen3.8-Flash-Next asks a different question.

What if you could get much more intelligence from much less compute?

If this really is the foundation for Qwen4 — things are about to get very interesting.

The biggest limitation of every AI coding assistant isn’t intelligence.

It’s not how much code it can right or how fast it responds.

It’s context.

What happens as software projects grow?

Conversations become longer, repositories become larger, and the amount of information the model needs to juggle explodes.

Architecture decisions, coding conventions, debugging discoveries, terminal output, documentation, and previous discussions all compete for the same finite context window.

That’s why Claude Code attacks this problem from multiple directions to generate the highest quality code possible.

It doesn’t just give you a larger context window — it provides wide of range of tools that intelligently manage what the model knows, remembers, and carries forward between sessions.

Let’s look at five of most powerful context management features, from quick context branching, to automatic features that most developers take for granted.

1. Your context shouldn’t increase with every new message

I made this mistake a lot in the past.

I would ask Claude Code a lot of small, unrelated questions in the middle of a long coding session.

Questions that would become a permanent part of the context — despite having no long-term value beyond the moment I asked them.

I didn’t realize how much context I was wasting.

But thankfully we have now have the /btw command to fix this exact problem in Claude Code.

It lets you ask a quick side question that has full visibility into your current conversation without adding the question or answer to your chat history. Instead, Claude shows the response in a temporary overlay that disappears when you’re done.

Asking questions on our changes with /btw:

That means you can ask things like:

• “What was that config file called?”
• “Why did we choose this approach?”
• “Which function handles authentication?”

…without cluttering your main conversation.

When we press Enter, the btw message disappears and we’re back to our normal conversation:

For long-running coding sessions, /btw helps keep your context focused on the implementation while still giving you instant access to everything Claude already knows about your project

2. Your Claude Code sessions don’t have to start from zero

You don’t have to being every new conversation with Claude Code from scratch.

Its Auto Memory system allows Claude to accumulate useful project knowledge over time.

As you correct mistakes, establish workflows, or repeatedly teach it project-specific conventions, Claude can save those learnings automatically and reload them in future sessions.

Instead of repeatedly explaining things like:

• build commands
• debugging workflows
• environment quirks
• preferred implementation patterns

Claude gradually learns them itself.

Over time, the assistant becomes increasingly tailored to your project with almost no manual effort.

3. Claude can’t figure everything out by itself

You can teach Claude Code how it’s done.

If Auto Memory learns automatically, CLAUDE.md is where you teach Claude deliberately.

It’s like permanent briefing document for your project.

Rather than re-explaining your coding standards, testing strategy, deployment workflow, or naming conventions every session, you simply write them once inside a CLAUDE.md file.

A sample CLAUDE.md file for Claude Code:

Every new conversation begins with that context already loaded.

A good CLAUDE.md might include:

• coding conventions
• important design decisions
• common team-specific workflows
• “always” and “never” rules

The more mature a project becomes, the more valuable this file gets.

4. What does Claude Code do when context gets out of hand?

Most AI tools simply start forgetting earlier parts of the discussion as context approaches the limit.

Claude Code does something much smarter — automatic context compaction.

As the context window fills up, it automatically compresses older portions of the conversation into a concise summary while preserving the important decisions, discoveries, and reasoning that led there.

Instead of carrying hundreds of thousands of tokens forever, Claude keeps the essential information while freeing space for new work.

This allows coding sessions to continue far longer without suffering the dramatic quality drop that often occurs when context windows become overloaded.

5. What can *you* do when context gets out of hand?

Claude doesn’t have sole control — you can also take manual control of your context — with multiple commands.

The /compact command tells Claude to summarize the current conversation immediately.

It’s perfect after finishing a large feature or debugging session. You preserve everything important while dramatically reducing context usage before moving on to the next task.

But somethings you don’t even want any previous conversation at all.

That’s where /clear comes in.

It starts a fresh conversation with an empty context window while still keeping your persistent project knowledge—such as CLAUDE.md instructions and Auto Memory—intact.

Think of it as wiping the whiteboard clean without forgetting everything you’ve learned about the project.

Claude Code’s approach isn’t just about having more context — it’s about using context intelligently.

Together all these features let Claude spend its attention on what matters most, making long-running development sessions feel remarkably consistent even as your projects grow in size and complexity.

Wow so we just discovered who was behind the incredible Ox Alpha stealth model all along!

It turned out to be none other than GLM-5.3 Flash from Z.ai in the end — so many Google fanboys were really disappointed from it not being Gemini.

It’s an absolutely incredible model that completely destroys its GLM-5.2 predecessor and other top-tier models — while being several times faster.

GLM-5.3 Flash has one of the lowest hallucination rates of all the models — and it’s more powerful than all the models in this list with lower hallucination rates:

GLM-5.3 Flash ranked an incredible 4th place on the AI Arena web dev leaderboard — despite several times cheaper than all the models above it:

“GLM-5.3-Flash delivered a major leap on DeepSWE v1.1, scoring 63.4 versus GLM-5.2’s 46.2 and outperforming Claude Opus 4.8 (58.0) and DeepSeek-V4-Vision-Exp (59.3), while coming within reach of Gemini 3.7 Flash (65.3) and GPT-5.6 Terra (69.6) — a remarkable result for an open-weight model built around efficient inference.”

And the shocking thing is that it does all this while being more than 900% more efficient than GLM-5.2 in operating costs.

GLM-5.3-Flash delivers a remarkable leap in cost efficiency, operating at roughly one-tenth the cost of GLM-5.2 — an improvement of more than 900% in operating-cost efficiency

And the secret behind this massive improvement lies in its state-of-the-art architecture.

GLM-5.3 Flash uses a new sparse attention technique to dramatically improve how the underlying transformer model processes text.

By combining linear attention for efficient local processing with sparse attention for selective long-range retrieval, GLM-5.3-Flash maintains strong long-context performance while significantly reducing the computational cost of traditional attention

Z.ai also built something called IndexPool to drastically reduce the amount of memory it uses — making it more than 4 times more efficient than even the standard GLM-5.3.

Through IndexPool, GLM-5.3-Flash compresses key representations across long contexts, cutting attention computation by roughly threefold and shrinking the KV cache by 4.4× compared with GLM-5.3 — significantly reducing the computational and memory costs of long-context inference.

Then there’s the Mixture-of-Experts design which lets it only activate the actual parameters it needs for every task.

Through its Mixture-of-Experts architecture, GLM-5.3-Flash contains 320 billion parameters while activating only 18 billion for each token — delivering the capabilities of a massive model without requiring its entire parameter set for every computation

So with all this you can definitely see why its so much more efficient than so many other models.

And with all this innovation and intelligent, it still comes with completely open weights — with the highly permissive MIT license.

Released as fully open weights under the permissive MIT license, GLM-5.3-Flash gives developers broad freedom to deploy, modify, and commercialize the model, with native integration across leading inference frameworks including SGLang and vLLM

So you can download and run it locally — though this one is definitely way too massive for an everyday PC.

And there’s something really fascinating that happened.

Before the official launch Z.ai secretly released GLM-5.3-Flash as Ox Alpha through OpenCode and OpenRouter. Millions of developers tested the model without knowing who was behind it.

And now Z.ai is saying that they processed all that traffic on state-of-the-art Chinese-made AI chips.

And it’s not just about the chips themselves — but how they made the made the model run on the chips.

They used sophisticated techniques like quantization and parallelism — to reduce memory usage and elevate performance.

By combining W8A8 quantization, tensor parallelism and specialized worker pools for different stages of inference, Z.ai built a highly optimized serving system that improved end-to-end performance by 3× while making large-scale deployment significantly more efficient

Z.ai says GLM-5.3 helped its engineers improve a lot of code and infrastructure they used to run GLM-5.3-Flash.

That means we are also getting closer and closer to the possibly ominous singularity stage — where AI starts becoming intelligent enough to improve itself — which leads to an more intelligent AI that can improve itself even better and so on.

All in all this is going to be such a game changing model in the AI ecosystem.

This is incredible.

Anthropic just made an insane change to Claude Code that will massively improve your productivity as a developer — although it might seem scary at first.

Claude Code now automatically executes commands and make tools calls by default when coding — no more stopping to ask for permission.

It won’t do this anymore:

And I know at first glance some developers will be seriously worried about this — won’t this potentially lead to disaster like file/database wipeouts or corruption from unsafe tool calls?

But actually this line of thinking is completely wrong — especially from what Anthropic discovered from their internal research.

In internal testing conducted by Anthropic across 1,053 paid participants, humans caught disguised dangerous commands only 13.6% of the time. After 50 consecutive permission prompts, detection dropped to roughly 5% as approval fatigue kicked in.

So it looks like it’s actually we humans we need to be concerned about when it comes to verifying commands.

So Claude Code now uses a background classifier to automatically know which commands are safe and which aren’t.

And the difference was absolutely insane — the automatic system was almost 7 times better at catching dangerous commands

Claude Code’s background classifier demonstrated a substantial security advantage, identifying and blocking 89% of destructive commands that might otherwise have been executed.

So of course once they saw the massive improvement it only made sense to make it the default in versions of Claude Code — Pro, Max, and Team.

And this change gives us so many benefits beyond just security.

Claude Code is no longer going to stall in its execution just to ask you to approve a command — you can finally experience the full value of long-horizon autonomous coding.

Anthropic reports that teams using it ship roughly 25% more pull requests because long-running agents can continue working without repeatedly waiting for human approval.

I found it pretty annoying at times — I would give Claude a prompt and expect it to be done when I come back — only to find out that it has barely even done anything — all because it’s waiting for me to approve a command.

I’m not the only one too:

Now I will have complete certainty that when I come back I will see results.

And also the background classifier is completely free — zero cost on all the complex logic or token usage of checking if a command is safe or not.

And free for all plans too.

Anthropic fully absorbs the computational cost of Claude Code’s background classifier, allowing Pro, Max, and Team users to benefit from continuous automated security checks without consuming any additional usage allocation.

So you don’t need to spend more just to be able to code with peace of mind.

And Auto Mode isn’t only looking for obviously dangerous shell commands — also helps defend Claude against indirect prompt injection.

So like if Claude reads a third-party package, webpage, or scraped file that has hidden instructions telling it to ignore the user and steal credentials.

Claude could potentially interpret those instructions as legitimate.

So this new Auto Mode adds another security boundary — the classifier always makes sure to compare Claude’s proposed action with the user’s original intent.

And this is especially important for autonomous agents because the more information they take in from the outside world, the larger their prompt-injection attack surface becomes.

Of course an AI supervising another AI creates its own problem — what happens when the security classifier repeatedly blocks Claude due to false positives?

Anthropic took this into consideration and built in an escalation mechanism.

If the classifier blocks three consecutive commands or reaches 20 blocked actions during one session, Claude Code automatically exits autonomous execution and returns control to the human.

One rejection might simply mean Claude chose a risky approach.

Twenty means something probably deserves human attention.

Auto Mode isn’t perfect tho — Anthropic still recommends human oversight for high-stakes production operations.

But the direction is really fascinating.

We originally put humans in the loop because we didn’t trust AI — then humans started clicking “Approve” without reading.

So now Anthropic has decided to put another AI in the loop to supervise the first one.

The new Grok 4.6 model is amazingly good at coding.

Elon Musk’s xAI trained it on billions of real developer interactions and and code editing patterns — thanks to their recent acquisition of the Cursor IDE.

It now performs as good as top-tier models like GPT-5.6 and Opus 5 the vast majority of the time — yet it’s multiple times cheaper.

And Grok 4.7 is already coming very soon too — with big promises of it “exceeding all current models” from Musk.

Grok 4.6 scores an incredible 5th place on the AI Arena Leaderboard — despite being much cheaper than most of the model above it — only Qwen 3.8 Max is as cheap as it (same price).

On CursorBench v3.2, Grok 4.6 Extra High took the top spot with 70.8%, edging out Fable 5 Max at 70.5% and Opus 5 Max at 70.0%, while comfortably beating GPT-5.6 Sol Max at 67.2%

It comes with major upgrades in software engineering and long-horizon agentic coding.

Grok 4.6 combines frontier-level benchmark performance with aggressive pricing, long-running agent capabilities, deep Cursor integration, and stronger visual application development.

It can carry out the most complex tasks in the most complicated codebases imaginable.

Grok 4.6 is designed to execute extended, multi-step workflows. It can use tools, navigate large codebases, research unfamiliar topics and maintain context across long sequences of actions.

It also has much stronger self-verification.

Grok 4.6 is much better at checking its own homework: xAI reports more self-testing and verification on long-running tasks, alongside a jump from 54% to 65.9% on DeepSWE v1.1 and 47.1% to 57.5% on APEX-Agents versus Grok 4.5.

And this fixes one of the biggest weaknesses of autonomous AI agents: small mistakes accumulating across long workflows.

It also has a 500K context window — something I was really surprised about — cause these days 1 million is like the standard. But it’s still quite a lot.

With a 500,000-token context window, Grok 4.6 can keep roughly 375,000 words of text in context at once—equivalent to about five full-length novels—giving long-running agents far more room to work without losing the plot

It’s a lot more intelligent than it’s predecessor — even though it costs exactly the same — giving much more intelligent per unit cost.

It scores 61 on the Artificial Analysis Intelligence Index, up from Grok 4.5’s 56 and placing it among the highest-performing models in SpaceXAI’s published comparison.

Grok 4.6 was trained from the ground up on the actual real-world code and developer patterns from Cursor — so it could achieve the best results possible as a coding agent.

Its training included high-quality engineering data and supervised fine-tuning trajectories covering software engineering, reasoning, STEM and agent workflows.

It used sophisticated techniques like reinforcement learning to deeply understand how we actually code and work as developers.

SpaceXAI then applied agentic reinforcement learning across environments including general coding, kernel optimization, web development and computer-aided design.

It also amazing at coding with visual input data — which makes it especially powerful for web design and frontend development.

Give it a high-level product idea, and the model can establish an application’s structure and visual language, implement its core interactions and then refine the result through subsequent iterations.

You can build unbelievably sophisticated games and 3d web experiences with very little prompting.

You can just describe what you want to build and let the Grok-4.6-powered agent work out all the architecture, interface and implementation.

xAI is still a serious contender in the AI race — and Grok 4.6 is definitely worth giving a try, for coding and many other use cases.

This is incredible.

Claude Code just got a new command that makes it so much easier to build beautiful and breathtaking apps.

The new /design command brings all the most powerful features of Claude Design directly into Claude Code –to make designing an app as effortless as possible.

It’s perfect for whenever you want to add a new visualizable feature like a new screen.

The /design command in action in Claude Desktop — works in Claude Code too:

Before you’d just ask Claude to build the feature and then it gives the result — which you may or may not be satisfied with.

But now things have gotten a lot more sophisticated.

Now with the /design command Claude can generate and show you several visual directions in which it can take to implement what you’re asking:

The /design command makes multiple design suggestions:

You can inspect and refine them — then turn the direction you choose into frontend code.

The most interesting part of /design is that it doesn’t have to commit to a single interpretation of your prompt.

Claude can generate several distinct visual directions on one canvas. One dashboard might be dark, dense, and technical. Another might be minimalist and editorial. A third one might emphasize large charts and simplified navigation.

The artboards can also be interactive rather than static mockups.

Forms, calculators, charts, and other UI elements can demonstrate the behavior for you — giving you a chance to test the micro-interactions before you commit to an implementation.

You can also make lightweight edits directly on the canvas like adjusting text, spacing, layout, or individual elements. That makes the design itself part of the conversation with Claude.

Adjusting the generating design with auto-generated tweaks:

Another amazing ability it has is how it resolves all the unclear requirements before it starts generating — it doesn’t just make irritating assumptions.

A vague request like “make me a modern SaaS dashboard” leaves dozens of decisions unspecified: typography, density, brand feel, navigation style, and overall visual direction.

Instead of silently falling back to familiar AI design clichés the design workflow can ask targeted questions about themes, layout preferences, branding, and constraints first.

And the best part is how it can you use your existing codebase and all the visual components in as context.

Claude can inspect your project and figure out all the styling conventions, CSS variables, Tailwind configuration, design tokens, typography, and reusable UI components.

So instead of generating a mockup that looks completely detached from the current application, /design can now work directly with your existing design language.

There’s no more need to export a screenshot, manually rewrite a design specification, or describe the chosen layout all over again.

That means the all type scale, spacing, component choices, and styling you select can flow directly into your frontend code.

It makes experimentation so much easier — and drastically expands the limits of your creativity.

Making it incredibly useful as a solo developer where you have to be a product manager, designer, and engineer at the same time.

The crazy thing is that nobody knows exactly who is behind it — just a cryptic name of Ox Alpha.

But it’s already scoring amazingly in several benchmarks — even beating Claude Fable and GPT-5.6 Sol in critical areas.

An initial evaluation using 10 DeepSWE coding tasks found that Ox Alpha completed eight, producing an 80% result on that subset. In the same comparison, Claude Fable 5 scored 65%, GLM-5.3 62%, Grok 4.6 62%, and GPT-5.6 Sol 52%.

Some people think it’s Gemini 3.5 Pro or 4, others are saying it has to be GLM-6.

But they’re literally offering the first 100 trillion tokens of usage for free — so clearly they have to be a huge company with deep pockets.

Backed by a provider claiming an unprecedented serving capacity of 100 trillion tokens per day, Ox Alpha offers near-unlimited free inference at an industrial scale that allows developers to run massive agentic workflows and long-horizon tasks without compute bottlenecks

And of course it has a 1 million token context window — something that’s quickly becoming standard across models.

Maybe they temporarily removed the “safeguards” to avoid being detected like this? 😅

One of Ox Alpha’s biggest features is its 1,048,576-token context window, alongside an output limit of approximately 131,072 tokens.

So it will have more than enough room for all your massive codebases.

That makes it particularly interesting for AI agents. Developers can feed the model substantial software repositories, documentation, issue histories, and specifications in a single context.

It also has full multimodal reasoning.

Ox Alpha isn’t limited to text. It natively supports text, images, and video, alongside tool calling and structured outputs.

So for example you can provide a UI wireframe and ask the model to implement it, or feed it a video recording of a software bug instead of explaining the problem manually.

It also uses the sophisticated Mixture-of-Experts (MoE) architecture — which let’s it achieve the intelligence of a massive model at a fraction of the computational cost and latency.

By deploying a Mixture-of-Experts architecture that routes tokens through approximately 40 billion active parameters out of a massive 744 billion total parameter pool, Ox Alpha delivers the world-knowledge capacity of a ultra-large model with the high inference speed needed to process up to 100 trillion tokens daily

I’ve really been noticing this growing trend of releasing stealth AI models — and I can definitely see why they do this.

When you don’t know who exactly is behind the model, you’re less likely to suffer from a preconceived bias of models from a certain company being better or worse.

Releasing a model without revealing its creator gives AI companies an enormous blind test. Developers benchmark it, discover weaknesses, stress-test its infrastructure, and compare it with competitors without being influenced by the company behind it.

And of course it’s also great for marketing — especially in a case like this that the model is doing particularly well — like a mysterious genius that every is going crazy about.

It also creates powerful marketing. Instead of announcing Ox Alpha with a conventional launch event, its creators have given the internet a mystery to solve.

It’ll be really interesting to see who the creators end up being and what more they have in store for us and the AI race.