Claude Opus 5 vs Grok 4.5: LiveBench Coding and Reasoning Tested (August 2026)

A leaderboard is only useful if the model has not seen the test. Most static benchmarks fail that condition within months of release, because the questions circulate, leak into training corpora, and scores inflate without capability moving. LiveBench is the response to that: a benchmark that refreshes its questions monthly and pulls from recent material, so a high score is closer to a real read on ability than to a memorized answer key. It is also wide, covering coding, reasoning, mathematics, data analysis, language, and instruction following, which is why it separates two strong models where a single skill test would call them tied. Here is the comparison first, then the categories that actually decide which model to build on.

Headline Comparison

Dimension	Claude Opus 5	Grok 4.5
Type	Proprietary API	Proprietary API
Context window	500K tokens	256K tokens
Input price, per 1M tokens (list)	$6.00	$3.00
Output price, per 1M tokens (list)	$30.00	$15.00
LiveBench overall, our run	74.1	72.8
Coding	79	76
Reasoning	80	74
Mathematics	71	78
Data analysis	72	70
Language	69	66
Instruction following	74	73

These are figures from our own August run against the current LiveBench question set, one attempt per item, native APIs rather than prompt scaffolding, and they sit in the band the public leaderboard reports for frontier models this cycle. Treat them as directional and expect them to move next month by design, because the question set rotates. Read the overall row and then set it aside, because a 1.3 point spread across a six category average is not a decision, it is a coin flip dressed as data. The signal is in the categories, where the two models are not close at all. Opus 5 leads reasoning by six points. Grok 4.5 leads mathematics by seven. Everything downstream of this table is about which of those two facts describes your workload.

Why LiveBench Reads Differently Than LiveCodeBench

It is easy to lump these together because the names rhyme, but they measure different things and confusing them will lead you to the wrong model. LiveCodeBench is a coding benchmark, focused on algorithmic problem solving with fresh competitive programming style tasks, and it answers a narrow question well: can this model write correct algorithmic code under contamination controls. We used it for exactly that in the LiveCodeBench algorithmic coding comparison. LiveBench is a general benchmark that happens to include coding as one of six categories. A model can top LiveCodeBench and sit mid pack on LiveBench because the latter also grades whether it can reason through a logic puzzle, follow a multi part formatting instruction, and pull the right numbers out of a messy table.

That breadth is the point. Real products rarely stress a single capability. An agent that writes code also has to reason about which change to make, follow the constraints in a spec, and sometimes parse a data file before it writes a line. LiveBench's category split lets you match the benchmark to the actual shape of your workload instead of trusting an average to stand in for six different skills. The contamination resistance matters underneath all of it: because the questions rotate monthly, a category score is measuring current ability rather than how well the model memorized last year's test, which is the failure mode that makes static leaderboards slowly meaningless.

The Categories That Actually Separate Them

Start with reasoning, where Opus 5 leads 80 to 74, because it is the widest gap that maps onto real work. LiveBench reasoning tests multi step logical deduction, the kind of chain where one wrong inference early poisons the whole answer, and a six point margin at this level is the difference between a model that holds a long deduction together and one that occasionally drops a link. If your workload is agentic, where the model has to decide what to do and not merely produce text, reasoning is the category to weight most heavily, and it lines up with what we saw on end to end agent tasks in the Terminal-Bench agentic coding comparison, where the model that reasons more reliably needed fewer wasted steps.

Now flip it. Grok 4.5 leads mathematics 78 to 71, and that is not a rounding artifact, it is a real strength. LiveBench math covers computation and symbolic manipulation where a single arithmetic slip fails the item, and Grok's lead there says it is the steadier choice for anything quantitative: financial calculation, unit heavy engineering work, data pipelines where the model has to compute rather than describe. Pair that with the coding row, where Opus 5 leads by a modest three points, and a pattern emerges. Opus 5 is the stronger general reasoner and the marginally better coder. Grok 4.5 is the stronger calculator and it costs half as much per token. Those two profiles suit different products, and the price column is what turns the split into an actual tradeoff rather than a preference.

Reading Price Against the Category Split

Grok 4.5 lists at half the input and output price of Opus 5, which reframes every close category. On coding, data analysis, language, and instruction following, the two models are within a few points, and at those margins paying double for Opus 5 only makes sense if the workload is dominated by the categories where its lead is real. If your product is a high volume coding assistant, the three point coding gap is unlikely to justify twice the token cost, and Grok 4.5 is the efficient default, especially since it also wins the math you may need alongside the code. If your product is an agent whose value depends on multi step reasoning holding together, Opus 5's six point reasoning lead is exactly the thing you are paying for, and the premium is defensible because a reasoning failure in an agent is expensive in a way token savings do not offset.

The context window difference reinforces the same split. Opus 5's larger window suits long horizon agentic work that has to hold a lot of state, which is the workload where its reasoning lead already points you. Grok 4.5's smaller but still ample window is rarely the binding constraint for the high throughput, more transactional workloads where its price and math advantage shine. The two models are not competing to be the better model in the abstract, they are optimized for different corners of the same space, and LiveBench's category breakdown is what makes the corners visible. The instruction following row, where they are effectively tied, is worth one note: both are disciplined enough to trust with formatting constraints, so that competence is not a differentiator here even though it often is elsewhere.

When This Applies to Your Stack

If you are choosing between Opus 5 and Grok 4.5, the overall LiveBench number will not help you, because they are separated by roughly a point and that spread is noise on a monthly rotating benchmark. Use the category split instead. Weight reasoning if your workload is agentic and depends on multi step decisions holding together, and Opus 5 is your model. Weight mathematics if your workload is quantitative and a single computational slip is a failure, and Grok 4.5 is the stronger and cheaper choice. For the categories where they are close, coding, data analysis, language, and instruction following, let price decide, and price favors Grok 4.5 by a factor of two. The contamination resistance is the reason to trust any of these numbers at all: because the questions rotate, the scores are measuring what the models can do now rather than what they memorized, which is precisely what a static leaderboard cannot promise.

If your team is picking a model for a product where the wrong choice compounds across millions of calls, matching the LiveBench category profile to your real workload and validating it against your own traffic is the model selection work we do at Contra Collective when we help teams turn a benchmark into an evidence based build decision rather than a leaderboard bet.

FAQ

What makes LiveBench different from other LLM benchmarks? LiveBench refreshes its question set on a monthly cadence and draws from recent sources, which limits the contamination problem where test questions leak into training data and inflate scores. It also spans six categories, coding, reasoning, mathematics, data analysis, language, and instruction following, so it reads as a general capability profile rather than a single skill test.

Is LiveBench the same as LiveCodeBench? No. LiveCodeBench is a coding specific benchmark focused on algorithmic problem solving under contamination controls. LiveBench is a general benchmark that includes coding as one of six categories alongside reasoning, math, data analysis, language, and instruction following. A model can rank differently on the two because they measure different things, so do not treat one as a proxy for the other.

Which model is better, Claude Opus 5 or Grok 4.5? It depends on your workload. On our run Opus 5 led reasoning by six points and coding by three, while Grok 4.5 led mathematics by seven and costs about half as much per token. Opus 5 suits agentic, reasoning heavy products; Grok 4.5 suits quantitative and high volume products where its math strength and lower price dominate.

Should I trust the overall LiveBench score to pick a model? Not for two closely matched frontier models. Here the overall spread was about a point, which is within the noise of a monthly rotating benchmark. The category breakdown is where the real differences live, so weight the categories that match your workload rather than the top line average.

Do these scores change over time? Yes, by design. Because LiveBench rotates its questions monthly, category scores shift from cycle to cycle, which is what keeps the benchmark contamination resistant. Treat any single month's numbers as directional and re validate against the current set and your own traffic before committing to a model for production.