Grok 4.5 Benchmarks, Costs, and Coding Performance

Cheaper than what, better at what? Those are the only two questions worth asking when a new AI model drops, and the answer is almost always delivered in the fine print rather than the announcement.

xAI's Grok 4.5 is genuinely cheaper than its main rivals. According to eesel.ai's benchmark breakdown, it undercuts both GPT 4.5 and Anthropic's Claude Opus 4 on pricing — sometimes substantially. Eric of the Eric Tech channel also referenced a large language model intelligence index placing Grok 4.5 competitively among current top-tier models, though that ranking reflects a single leaderboard snapshot and shouldn't be treated as a permanent verdict. Benchmark positions shift weekly in this industry.

The more interesting question is what "cheaper" actually buys you in practice — and that's where Eric's hands-on testing in OpenCode gets useful.

What OpenCode Is, and Why It Matters Here

OpenCode is a coding agent framework — think of it as an AI-powered co-pilot that doesn't just answer questions but executes sequences of tasks on its own: reading files, writing code, running tests, revising based on results. The key distinction from a basic chatbot is autonomy. You describe a goal; the agent works through the steps to get there without you holding its hand at each turn. OpenCode's specific appeal is that it's model-agnostic — you can swap in Grok 4.5, GPT 4.5, or anything else without rebuilding your setup. Whatever "skills" you've defined (reusable task templates, essentially) carry over regardless of which model is underneath.

Eric connected Grok 4.5 through xAI's SuperGrok subscription, which took a single authentication step. The operational friction was low — a point worth noting because switching costs are often understated when developers evaluate new models.

The Test That Actually Tells You Something

Rather than running Grok 4.5 through sanitized benchmarks, Eric gave it a live task: query a personal knowledge base built in Obsidian — a note-taking tool that links ideas the way a mind map does — synthesize the stored context, and then advise on a real business problem. He called this the "Ask the Board" skill: the model impersonates an advisory panel drawing on his actual notes, goals, and history to give strategic counsel.

He ran the identical prompt through both Grok 4.5 and GPT 4.5. The outputs were substantively similar — both models surfaced the same core recommendations. But what separated them wasn't the quality of the advice. It was the volume of text generated to deliver it.

Grok 4.5 used around 84,000 tokens to complete the task. GPT 4.5 burned through over 200,000. Tokens are the unit of text an AI model processes — roughly three-quarters of a word each — and they're also the unit you pay for. The two models reached the same destination, but one took a much longer route.

Eric put it plainly: "Grok 4.5 here tries to keep the output as concise as possible so that it actually consumes less token. But then you can see that GPT here actually consumes more token because the output here is actually longer."

That's not a bug in Grok 4.5. It's arguably a feature — provided the compressed output doesn't lose meaningful signal. In this test, Eric concluded it didn't.

Where GPT 4.5 Still Wins

The landing page test is where the picture gets more complicated, and I'd argue it's the more honest data point.

Eric asked both models to build a pet shop landing page from scratch. Grok 4.5 actually showed more procedural discipline — it triggered more of the defined skills in OpenCode's framework (brainstorming, planning, quality-checking) and asked clarifying questions before diving in. In theory, that's the right behavior. A model that stops to ask "what do you actually need?" before generating 500 lines of code is modeling good engineering practice.

The output, though, didn't match the process. Eric's verdict on what Grok 4.5 produced was direct: "It kind of feels quite AI slop." Static layout, no animation, nothing particularly coherent about the visual design.

GPT 4.5, by contrast, delivered a more polished result — hover effects, visual depth, multiple content sections. It triggered fewer skills during the process but generated something that looked like a real deliverable.

This is the tension that Eric's testing surfaces, and it's a real one. Good process doesn't guarantee good output. Grok 4.5 went through the right motions and produced the lesser result. GPT 4.5 skipped some steps and produced the better page. Anyone who's managed software teams will recognize the pattern immediately.

The Planning vs. Execution Split

Eric's conclusion is what I find most worth examining. His recommendation: use Grok 4.5 as the execution engine for routine tasks where cost matters, and reserve GPT 4.5 or Claude Opus 4 for the higher-stakes planning and generation work.

"Using Opus or GPT 4.5 for extreme high planning," he said, "and using something like Grok 4.5 for execution — that's going to be a big buck saving."

This two-tier model architecture is becoming a common pattern in AI development circles, and Grok's earlier iterations showed hints of it taking shape. The logic is sound on paper: not every task requires your most expensive model, and routing simpler execution tasks to a cheaper, faster one saves real money at scale. The risk — and this is worth naming — is that the boundary between "execution" and "planning" is rarely as clean in practice as it sounds in a YouTube summary. Developers who rigidly apply this split may find Grok 4.5 handling tasks that genuinely needed more capable reasoning, and the cost savings evaporate in debugging and rework.

That's not a knock on Eric's framework. It's a caveat the framework itself requires. The execution/planning split is a genuinely useful mental model, not a magic system. How well it works depends entirely on how accurately you've classified which tasks belong in which bucket — and that classification itself takes judgment.

What Grok 4.5 Actually Is

Strip away the benchmark charts and the subscription tiers, and what you have is a fast, cost-efficient model that performs at a level comparable to more expensive options for text-heavy reasoning tasks, and falls short on visually complex creative generation. Eric compared it to Claude's Sonnet tier — a mid-range workhorse rather than a flagship.

That's not a disappointing position to occupy. The AI model market right now is dominated by flagship pricing that makes sustained autonomous usage — the kind where an agent runs sequences of tasks over hours — genuinely expensive. A capable mid-tier model with solid process discipline and low token consumption has real practical value.

The honest summary is this: if your workload is heavy on analysis, synthesis, and text-based reasoning, Grok 4.5's efficiency advantage is real and the quality difference versus GPT 4.5 appears marginal. If your workload involves generating polished front-end output or anything where the visual and structural richness of the result matters, GPT 4.5 still earns its higher cost.

What neither model has fully solved — and what Eric's test inadvertently illustrates — is the gap between correct process and useful output. Grok 4.5 followed the right steps to build that landing page. The page still wasn't good. Until that gap closes, "it triggered all the right skills" remains a consolation prize.

---

Bob Reynolds is Senior Technology Correspondent at BuzzRAG.