Grok 4.5 has just released and the benchmarks are loud
It sits between GPT 5.5 and Opus 4.8, and looking at the cost comparisons it is way cheaper than both of them. On the large language model intelligence index, Grok 4.5 currently sits at number four. So yeah the numbers look good. But the real question I care about is: should you actually make the switch from whatever model you’re using right now inside your AI agent? Not just reading a leaderboard, actually dropping it into your second brain or your app builder and seeing if it does the job.
That’s exactly what I’ve been doing for the past 10 hours. I wired Grok 4.5 into my Open Code agent, ran it through the same skills and the same prompts I use with GPT 5.5, and watched the output, the token burn, and whether it actually triggers the right skills. Here’s the whole setup, the tests, and what I honestly think.
How to connect Grok 4.5 inside your own AI agent
If you haven’t used Open Code before, it’s like Claude Code or Codex but you can pick any model you want. All the frameworks, tools, agents, and skills you create stay consistent no matter which model you’re running. That’s why it’s my go-to for this kind of comparison.
First thing: open Open Code and type /connect. That opens a list of all the providers you can plug in.
You have two straightforward paths for Grok 4.5:
- Pay as you go with Open Code Zen. This works like OpenRouter, you load some credits, say $10, and it caps your usage at exactly that. No surprise bills.
- Use your SuperGrok subscription. Just search for “xAI” in the providers list, pick the SuperGrok subscriptions option, hit enter, and authorize the connection in a browser tab. It’ll ask you to allow access, and once you click approve, Grok 4.5 shows up right inside Open Code.
I went with the SuperGrok route. Hit enter on Grok 4.5 and boom, the agent was connected. I sent a quick “hi” prompt to make sure it was firing, and sure enough I got a response back straight away. Good to go.
Now the proper test.
Test 1: The board member skill, same prompt, different models
Inside my second brain I have a skill I built called the board member skill. The idea is simple: I take transcripts, profiles, and advice patterns from a famous entrepreneur or advisor, say Alex Hormozi, and turn them into a trained persona. Anytime I’m stuck on a hard business decision, I trigger that skill and it gives me a second opinion grounded in that advisor’s mindset, plus it has access to my own knowledge base so it references my actual context, not generic platitudes.
I’ve got my second brain organised in Obsidian (my contacts, raw folders, research, skills, the whole thing), and I ran the board member skill twice: once with Grok 4.5 and once with GPT 5.5, using the exact same prompt.
The prompt wasn’t polished. I just said something like:
“Based on what we have currently inside our context for how our business works and what our goal is, what I’m currently stuck on is growing my school community. Can you fetch the latest information from our school, pull it from the raw folder, synthesize it inside our context, and then answer why we’re stuck at a certain member count threshold and how we can improve it. Give me decisions and things we should start making right now.”
Both models spat out a decision structure. Grok 4.5 gave me a clear table right away, current state, business goal, advisory advice. The advisor inside Grok pulled my goal straight from my second brain context. The recommendations were along the lines of “show more wins” and “focus more on monetization courses for the level 5 members.” Solid points.
GPT 5.5 gave me pretty much the same advice, just wrapped in a longer output. The two models did not overlook anything the other one caught. It was a wash on quality.
The real surprise was the token consumption.
- Grok 4.5 used 84,174 tokens, filling only a fraction of the context window.
- GPT 5.5 chewed through over 200,000 tokens, hitting 51% of the context window for the same prompt and the same knowledge base references.
I read both outputs thoroughly. They said the same things in essence. GPT’s version was just longer. Grok explicitly seems optimized to be more concise, not because it skips steps, but because it keeps the output tight so it burns fewer tokens. That is a big deal when you’re running dozens of prompts a day through an agent.
Test 2: Building a landing page from scratch
I then told both models the same thing: “Based on what you know about my context, pretend my business today pivoted to a pet shop. Build me a landing page.”
No design brief, no wireframe. Just that prompt and whatever skills the agent decided to trigger.
Gro 4.5 went to work and triggered a bunch of skills right away: front-end design, brainstorming, writing plans, test-driven development, and even a skill called “impeccable” for the planning phase. It also asked me a ton of clarification questions, more than GPT 5.5 ever bothers with. I actually like that, because it means the model wants to pin down exactly what I need before it generates anything.
GPT 5.5 triggered fewer skills. It still used front-end design, brainstorming, writing plans, and test-driven development, but it skipped the impeccable skill entirely and asked far fewer questions. It just… built.
Now the outputs.
GPT 5.5’s landing page had a hero section, multiple tabs, some hover effects, and the SVG looked okay, nothing stunning, but there was a bit of animation going on. The page felt a touch more polished, although the glass look wasn’t great.
Grok 4.5’s page was functional but flat. Hero section, cards, footer. Zero animations. It felt, honestly, kinda like AI slop. The layout was clean but nothing you’d show a client.
So even though Grok triggered more skills, including the ones that are supposed to make the output better, GPT 5.5 produced the more feature-rich result. Not by a huge margin, but it was noticeable.
If I had to sum it up: Grok triggered the right skills to think through the process better, but the final polish wasn’t there. GPT triggered fewer skills, asked less, and still built something closer to what I’d actually deploy.
Why token cost changes the whole conversation
One thing that stood out across both tests: Grok 4.5 is dramatically cheaper in practice. It’s not just the per-token pricing on paper, it’s that the model itself tries to give you the output in fewer tokens. In the board member test, the output was short and actionable. In the landing page test, it stayed concise even when generating code.
When you stack this across dozens or hundreds of agent runs in a week, the savings are real. If I’m using a tier-one model like Opus 4.8 or even GPT 5.5 for planning and high-level thinking, and then I swap the execution layer to Grok 4.5, that’s big buck saving territory. We’re talking about turning a $200-a-month habit into something that might cost $40.
And that matters, because a lot of us aren’t just running a single prompt, we’re building second brains that get queried multiple times a day, or we’re using AI agents to scaffold apps, write tests, and research documents. Tokens add up.
Where Grok 4.5 actually fits in your AI stack
After 10 hours of using it, I don’t think Grok 4.5 competes with the top-tier models like Opus 4.8. Those models cost more and they really get you the results you want when the task is messy or creative. But Grok 4.5 isn’t trying to be that.
What it does well is act as a fast, cheap executor. Think of it as a replacement for Claude Sonnet 3.5, especially now that Sonnet 3.5 pricing is creeping up. Sonnet used to be the “get things done” model, reliable, quick, affordable. Grok 4.5 slots right into that exact role: an implementer that runs your skills, pulls your context, and fires back a tight answer without draining your credits.
Pair it with GPT 5.5 or Opus 4.8 for the planning phase, the bit where you need deep reasoning, multiple rounds of clarification, or creative leaps, and then hand the structured plan off to Grok 4.5 for execution. You get the best of both worlds without paying premium prices for every single API call.
The one nuance I noticed: Grok’s tendency to ask clarifying questions is a double-edged sword. In the board member skill, that wasn’t relevant because the prompt was already self-contained inside my knowledge base. But in the landing page task, all those extra questions slowed the loop. If you’re building an agent pipeline that calls the model 20 times in a row, you might not want that many interruptions. GPT 5.5 just barreled ahead and built a page, uglier in some parts, sure, but faster.
A quick side note on skills and reliability
Across both tests, Grok 4.5 triggered skills that GPT 5.5 completely ignored, like the “impeccable” planning skill. That’s interesting because those skills are deliberately crafted to enforce structure, writing plans, test-driven prompts, explicit checklists. If your agent relies heavily on those guard rails, Grok 4.5 might actually be the safer choice, even when the raw output isn’t as visually impressive.
It respects the system. It follows the sequence you’ve set up. That’s worth something when you’re building compound agents where each skill depends on the previous one firing correctly.
GPT 5.5, on the other hand, sometimes leaps over precautions. That’s what gives it speed, but it also means you might occasionally get a half-cooked answer that you have to re-prompt.
Key Takeaways
- Grok 4.5 sits between GPT 5.5 and Opus 4.8 on benchmarks but costs significantly less.
- Connecting it to Open Code is straightforward: either load credits via Open Code Zen or link your SuperGrok subscription.
- In a second-brain advice test, Grok 4.5 matched GPT 5.5’s quality but used less than half the tokens, 84k vs over 200k.
- In a landing page building test, GPT 5.5 produced a more polished output with animations and tabs, while Grok 4.5’s page felt basic and sloppy, even though Grok triggered more skills.
- Grok 4.5 is not a rival to Opus 4.8 or the top end. It’s a fast, cheap execution model that can replace Claude Sonnet 3.5 as your everyday implementer.
- The best setup is to use a strong planner (GPT 5.5 or Opus) for the hard thinking and let Grok 4.5 handle the execution to save tokens.
- Grok asks more clarifying questions by default, which is great for thoroughness but can slow down loops in autonomous pipelines.
I’m not saying rush out and replace everything with Grok 4.5. But if you’re building AI agents that hammer the model 50 times a day and your token bills are making you wince, this model deserves a slot in your stack. Try it as the executor behind a heavier planner and watch your costs drop while the output stays roughly the same.
What’s your take after using it? Drop a comment because I’m curious if others are seeing the same pattern, or if you’ve found a use case where Grok actually beats the big models outright.