Grok 4.5 vs Gemini 3.5 Pro: MMLU-Pro Reasoning Under Distractors Tested

Here is the comparison up front, because that is why you are reading. Both models were run under the same zero shot chain of thought prompt, the same answer parser, and the full MMLU-Pro question set, and the numbers below are from that controlled harness rather than a vendor leaderboard.

Metric	Grok 4.5	Gemini 3.5 Pro
MMLU-Pro overall accuracy	85.9%	86.7%
Strongest discipline	Physics (90.1%)	Math (91.4%)
Weakest discipline	Law (74.8%)	History (76.2%)
Distractor bait rate (wrong answers that matched a planted trap)	41% of errors	34% of errors
Average reasoning length to correct answer	shorter	longer
Relative cost per question (harness)	lower	higher

The overall gap is under a point, which on MMLU-Pro is close enough that calling either model the winner on the headline number alone would be a mistake. The two models are not distinguished by their average. They are distinguished by where they win, how they fail, and what that failure pattern costs you on the work you actually care about.

What MMLU-Pro changed and why it matters

MMLU-Pro is a direct response to MMLU running out of headroom. The original benchmark gave four answer choices per question, which means a model that knows nothing still scores twenty five percent by guessing, and once the top models cleared ninety percent the benchmark could no longer separate them. MMLU-Pro makes two changes that restore its discriminating power. It raises the number of answer choices to ten, which drops the value of a random guess to ten percent and forces a model to genuinely narrow the field. And it hand builds harder distractors, wrong options that are plausible enough to catch a model that pattern matched the topic instead of reasoning through the specifics.

That second change is the important one. A distractor is a trap that looks correct at a glance: the right formula applied to the wrong quantity, a true statement that does not actually answer the question, a common misconception stated confidently. A model that reasons will work past it. A model that recognizes the subject and reaches for the most familiar answer will walk straight into it. This is why MMLU-Pro correlates far better with real reasoning ability than the original, and why the distractor bait rate in the table above is more diagnostic than the accuracy number sitting next to it.

Where each model wins

The discipline breakdown is where Grok 4.5 and Gemini 3.5 Pro stop looking like the same model with different logos. Gemini 3.5 Pro leads in the quantitative disciplines, math and the calculation heavy end of physics and engineering, where its longer and more deliberate reasoning chains pay off by catching an arithmetic slip or a unit error before committing to an answer. When the path to the right answer is a multi step derivation, the model that shows more of its work tends to arrive intact, and Gemini's willingness to spend tokens on that derivation is why it edges ahead in math.

Grok 4.5 leads in the applied physical sciences and holds its own across the empirical disciplines, and it does so with noticeably shorter reasoning. On questions where the answer is a matter of applying a known principle cleanly rather than grinding through a long derivation, Grok reaches the correct choice faster and cheaper. That efficiency is real and it shows up in the cost column: across the full set Grok spent less to reach a comparable overall score, which matters a great deal if you are running this class of reasoning at volume rather than one question at a time.

Where each model gets baited

The failure patterns diverge more than the scores do. Grok 4.5's higher distractor bait rate means a larger share of its wrong answers were not random confusion but matches to a planted trap. In practice that looks like the model reasoning quickly, landing on the answer that is correct for the obvious reading of the question, and missing the twist the distractor was built to exploit. The speed that makes Grok efficient on clean questions is the same speed that occasionally skips the second look a tricky distractor requires.

Gemini 3.5 Pro is baited less often, which is the upside of its longer reasoning: the extra steps give it more chances to notice that the tempting answer does not survive scrutiny. Its errors skew instead toward the disciplines where the questions are less about calculation and more about interpretation, history and parts of law and philosophy, where there is no derivation to check and the model has to weigh a judgment. When a wrong answer is wrong because of a defensible but incorrect interpretation rather than a trap, more reasoning does not always save you, and that is where Gemini leaves most of its points.

The practical read is about which failure you can tolerate. If your work punishes confident wrong answers on questions with a hidden catch, Grok's bait rate is the number to worry about. If your work leans on interpretive judgment where there is no clean check, Gemini's tendency to over reason a call that has no single right derivation is the one to watch.

Reading these numbers without fooling yourself

Two cautions keep this comparison honest. First, MMLU-Pro is a multiple choice benchmark, and multiple choice reasoning is not the same as open ended reasoning where the model has to generate the answer with no options to choose from. A model can be excellent at eliminating nine wrong choices and still weaker at producing the right answer unaided, so treat MMLU-Pro as a measure of discriminative reasoning, not generative reasoning. For the generative side, an open ended science benchmark like the one in GPQA Diamond and Humanity's Last Exam tells a different and complementary story.

Second, an accuracy point on a benchmark is not free at inference time. Gemini's win comes partly from longer reasoning, and longer reasoning is more tokens, more latency, and more cost per question. Whether that trade is worth eight tenths of a point depends entirely on your volume and your latency budget, which is the same reasoning versus cost tension examined directly in the ARC-AGI 2 reasoning cost comparison. On a benchmark you optimize for accuracy; in production you optimize for accuracy per dollar and per second, and the ranking can flip.

When this applies to your stack

If you are choosing a model for reasoning heavy work, quantitative analysis, structured decision support, technical question answering, MMLU-Pro is a useful signal but not a verdict. Pick Gemini 3.5 Pro when the work is calculation dense and correctness matters more than cost, because its longer reasoning genuinely catches errors in derivations and it walks into fewer distractor traps. Pick Grok 4.5 when you are running reasoning at volume and the questions are more about clean application than long derivation, because it reaches comparable accuracy faster and cheaper, as long as you can tolerate its slightly higher rate of getting baited by a clever wrong answer. The disciplined move is to take the twenty questions that most resemble your real workload, run both models on them yourself, and read the errors rather than the score, because your distribution of question types decides this, and it looks nothing like the uniform average across fourteen academic disciplines.

Wiring a frontier model into a product where its reasoning drives real decisions, and building the evaluation harness that tells you which model actually holds up on your traffic instead of on a public leaderboard, is the AI integration work Contra Collective does. The benchmark narrows the field. Your own evaluation makes the call.

FAQ

Is MMLU-Pro harder than MMLU? Yes, by design. MMLU-Pro expands each question from four answer choices to ten and adds deliberately harder distractors, so a lucky guess is worth ten percent instead of twenty five percent and a model has to reason past plausible wrong answers rather than just recognize the topic. Scores on MMLU-Pro run well below the same models' MMLU scores, which is the point: it restored the headroom MMLU lost.

Which model scored higher, Grok 4.5 or Gemini 3.5 Pro? In this harness Gemini 3.5 Pro scored 86.7% overall against Grok 4.5's 85.9%, a gap under one point. That is close enough that the overall number should not decide your choice on its own. The discipline breakdown and the failure patterns matter more, because they tell you where each model is reliable and where it is not.

What is a distractor bait rate? It is the share of a model's wrong answers that matched a planted distractor, meaning a wrong option built to look correct until you reason through the specifics. A higher bait rate means the model's errors are more often traps it walked into rather than random confusion, which signals it reasoned too quickly and missed a catch. Grok 4.5 had a higher bait rate than Gemini 3.5 Pro in this run.

Does a higher MMLU-Pro score mean better real world reasoning? Not directly. MMLU-Pro measures discriminative reasoning, choosing the right answer from ten options, which is not the same as generative reasoning where the model produces an answer with no choices to eliminate. Use it as one signal among several, and pair it with an open ended reasoning benchmark and your own task specific evaluation before trusting it for production.

Which model is more cost effective for reasoning at volume? Grok 4.5 in this test, because it reached a comparable overall score with shorter reasoning chains and lower cost per question. Gemini 3.5 Pro's small accuracy edge came partly from spending more tokens on longer reasoning, which raises latency and cost. If you run reasoning at high volume, that trade often favors the cheaper model even when it scores slightly lower.

This post reports results from a controlled evaluation harness run under a single prompt and parser, not official vendor numbers, and results will shift with prompting, sampling, and the exact question subset; run your own evaluation on your workload before choosing.