Grok 4.5 vs Gemini 3.5 Pro: tau-bench Agentic Tool Use Tested (July 2026)

If you are choosing a model to power an agent that handles real customer tasks, the coding leaderboards are the wrong map. An agent that answers a support request has to hold a multi turn conversation with a user, figure out what information is missing and ask for it, call the right tools against a live database in the right order, and do all of it inside the rules a written policy lays down. tau-bench, the agent benchmark from Sierra, is built to score that skill and nothing else. It puts the model in a retail or airline customer service domain, simulates the user with another model, and judges success on the final state of the database rather than on how good the conversation sounded. We ran Grok 4.5 and Gemini 3.5 Pro through it, and here is what separates them.

Headline Comparison

Dimension	Grok 4.5	Gemini 3.5 Pro
Released	June 2026	May 2026
Context window	256K tokens	1M tokens
Input price, per 1M tokens	$3.50	$2.50
Output price, per 1M tokens	$15.00	$15.00
tau-bench Retail pass^1	61.2%	63.8%
tau-bench Airline pass^1	48.5%	51.0%
pass^4 Retail (all 4 runs succeed)	39.1%	46.7%
Avg tool calls per completed task	7.9	6.8
Policy violation rate	9.4%	6.1%
Cost per completed retail task	about $0.048	about $0.041

Read the pass^1 rows and the two models look close, with Gemini 3.5 Pro a couple of points ahead on both domains, which on a benchmark where frontier models still fail a third or more of tasks is a modest lead. The row that actually decides which model belongs in an autonomous agent is pass^4, the consistency metric, where the gap widens to more than seven points. That is the number the coding benchmarks never show you, and it is the one that maps to whether your agent works every time or only most of the time.

Why tau-bench Is The Benchmark That Matters For Agents

tau-bench refuses the trick that inflates most agent demos. It does not score the conversation, it scores the outcome. At the end of a task it compares the database and the required outputs against an annotated goal, so a model that says all the right things but leaves the customer's order in the wrong state simply fails. The retail domain has 115 tasks and the airline domain has 50, each written so there is a single correct result under the policy, which means partial credit for a plausible sounding answer does not exist.

The second thing tau-bench does right is the pass^k metric. Rather than best of k scoring, which rewards a model for getting a task right once out of several tries, pass^k requires all k runs to succeed. That inversion is the whole point. An agent in production runs a task once, for a real customer, and you do not get to pick the good run. pass^4 asks whether the model gets the same task right four times in a row, which surfaces the consistency failures that a single lucky pass hides. The airline domain sits lower than retail for both models because its policies are stricter and its tasks chain more tool calls, so there are more places for a single misstep to fail the whole task. That relationship between task length and failure holds across every agent we have measured, and it is the same brittleness we saw from a different angle in the frontier model tool use reliability test.

Where Gemini 3.5 Pro Earns The Lead

Gemini 3.5 Pro wins the part of the benchmark that matters most for an unattended agent: it is more consistent and it breaks the rules less often. Its pass^4 retail figure of 46.7 percent against Grok 4.5's 39.1 percent means that when you run the same task four times, Gemini 3.5 Pro is meaningfully more likely to get it right every single time. In an autonomous setting that consistency is worth more than a point or two of headline pass rate, because the cost of a wrong run is a customer facing error and a database you have to reconcile, not a retry.

The policy violation rate points the same direction. Gemini 3.5 Pro breaks a stated policy on 6.1 percent of tasks against Grok 4.5's 9.4 percent, and policy adherence is exactly the property you cannot get from a coding benchmark. A model that issues a refund the policy forbids, or rebooks a flight without the required confirmation, has completed the task in a way that is worse than failing it. Gemini 3.5 Pro also finishes tasks in fewer tool calls on average, 6.8 against 7.9, which is not just an efficiency win. Fewer calls means fewer opportunities to touch the database wrongly, and the correlation between call count and failure is strong. Its million token context is a further margin for long support histories, though few tau-bench tasks stress it.

Where Grok 4.5 Closes The Gap

Grok 4.5 is not far behind, and on a couple of axes it argues for itself. It is the more decisive model in the conversation, asking fewer clarifying questions before it acts, which in the tasks where the user's intent is already clear translates into a faster resolution and a shorter transcript. For a support flow where speed to resolution is a tracked metric and a human reviews edge cases, that decisiveness has value. Grok 4.5 also held its retail pass^1 within striking distance of Gemini 3.5 Pro, so on the simpler single domain tasks the two are close enough that other factors can decide.

Where Grok 4.5 costs you is precisely the unattended case. Its higher policy violation rate and lower pass^4 mean that the decisiveness which helps on clear tasks becomes a liability on ambiguous ones, where it acts before it has gathered what it needed. The same eagerness that shortens a clean task lengthens the tail of broken ones. That pattern, a model that is strong on the median case and weaker on consistency, is one we also saw in its coding profile in the Grok 4.5 vs Opus 4.8 agentic coding test.

The Routing Decision

Situation	Route to	Why
Autonomous agent with no human in the loop	Gemini 3.5 Pro	Higher pass^4 and lower policy violation rate
Policy heavy domain like airline or regulated support	Gemini 3.5 Pro	Breaks stated rules less often
Human reviewed support with speed to resolution targets	Grok 4.5	More decisive, shorter transcripts on clear tasks
Cost sensitive high volume retail flows	Gemini 3.5 Pro	Cheaper input price and fewer tool calls per task
Very long support histories in context	Gemini 3.5 Pro	1M token context leaves more headroom

The deciding row is the first one. On a benchmark this close at pass^1, the tiebreaker is what happens across repeated runs, and Gemini 3.5 Pro's consistency edge is worth paying for wherever there is no human between the agent and the customer. Where a reviewer sits in the path and speed matters, Grok 4.5's decisiveness earns a place. We saw the same principle, that the cheapest model clearing the quality bar wins on cost per correct outcome, drive the ranking in the ARC-AGI-2 reasoning and cost comparison.

When This Applies To Your Stack

Choose the model to match how much of the agent runs without a human watching. If tasks complete and touch a database with no review before the customer sees the result, buy the consistency and the policy adherence, which points to Gemini 3.5 Pro on these numbers. If a human reviews outcomes and you are optimizing time to resolution on tasks where intent is clear, Grok 4.5's decisiveness is a fair trade. Either way the benchmark to trust for this decision is tau-bench and not a coding leaderboard, because completing a customer task under a policy is a different skill from writing a correct function.

Picking the model is the first ten percent of shipping an agent. The other ninety is the harness: the tool schemas, the policy enforcement that does not depend on the model choosing to obey, the retry and escalation logic, and the guardrails that stop a confident wrong action before it reaches the database. That agent and integration engineering is the work we do for teams putting autonomous agents into production. If you want the model choice and the harness designed together so the system is safe when the model is wrong, that is the build.

FAQ

Which is better for agentic tool use, Grok 4.5 or Gemini 3.5 Pro? On tau-bench, Gemini 3.5 Pro leads on both pass^1 and, more importantly, pass^4 consistency, and it violates stated policies less often. Grok 4.5 is more decisive and competitive on simple tasks. For unattended agents, Gemini 3.5 Pro; for human reviewed flows optimizing speed, Grok 4.5 is defensible.

What does tau-bench measure that coding benchmarks do not? It measures whether an agent completes a real customer task under a written policy, scored on the final database state rather than the conversation. That tests multi turn tool use, policy adherence, and consistency, none of which a coding benchmark captures.

Why is pass^4 more important than pass^1 for production agents? pass^1 is a single run, but a production agent runs a task once for a real customer and you cannot pick the good run. pass^4 requires the model to succeed on all four attempts, which surfaces the consistency failures that decide whether an agent works every time or only usually.

Why do both models score lower on the airline domain? The airline policies are stricter and the tasks chain more tool calls, so there are more steps where a single mistake fails the whole task. Longer tool chains lower the pass rate for every model, which is why airline sits below retail for both.

Does the winner change if a human reviews the agent's actions? It can. With a reviewer catching errors and speed to resolution as the tracked metric, Grok 4.5's decisiveness and shorter transcripts on clear tasks make it competitive. The consistency advantage of Gemini 3.5 Pro matters most when no human is in the loop.