Gemini 3.5 Pro vs Grok 4.5: GAIA General Assistant Agentic Tool Use Tested (August 2026)

A model that wins on a reasoning benchmark can still be a poor assistant, because an assistant's real job is finishing a task that takes many correct steps, not answering one hard question in isolation. GAIA is built to test that skill and not the other one. Its questions are easy for a competent person and hard for a model, because they demand action: search the web, open an attached file, read the relevant row, compute something from it, and carry that result forward, often across ten or more tool calls. There is one verifiable answer per question and no partial credit, so a single wrong step anywhere in the chain fails the whole task. Here is the comparison first, then the step and tool split that decides which model to actually build on.

Headline Comparison

Both models ran against the GAIA validation set through the same agent harness: the same tools (web search, page reader, file parser, calculator, Python sandbox), the same maximum step budget, one attempt per question, and identical prompts. The only variable was the model doing the planning and the tool calls.

Metric	Gemini 3.5 Pro	Grok 4.5
GAIA overall accuracy	68.9%	65.4%
Level 1 (few steps)	87.2%	85.6%
Level 2 (several steps, multi tool)	66.1%	61.8%
Level 3 (long chains, hardest)	41.3%	39.7%
Avg tool calls per solved task	6.8	7.9
Valid tool call rate	97.4%	94.1%
Median wall clock per task	41s	33s
Price per 1M input / output tokens	$2.00 / $12.00	$1.20 / $7.50

The overall gap is under four points, which is close, but the interesting numbers are not the accuracy row. Gemini 3.5 Pro solves tasks in fewer tool calls and issues valid calls more reliably, while Grok 4.5 is faster per task and cheaper per token. That combination is the whole story: one model is more efficient per task, the other is quicker and cheaper per step, and which one wins for you depends on how long your tasks run and who is paying for the retries.

Where the Steps Are Lost

GAIA's no partial credit rule means failure analysis is really about where in the chain each model breaks, and the two break differently.

Gemini 3.5 Pro's advantage grows with chain length. On Level 1 questions, which resolve in a couple of steps, the two models are within two points, because there is little room for a plan to go wrong. The gap widens at Level 2 and holds at Level 3, and the reason shows up in the tool call statistics: Gemini 3.5 Pro reaches an answer in fewer calls and wastes fewer of them on malformed or redundant actions. Its 97.4% valid call rate means it rarely burns a step on a tool invocation the harness rejects, and each wasted step in a long chain is a chance for the plan to drift. When it fails, it usually fails on genuine reasoning: a question that needed an inference it did not make, not a tool it fumbled.

Grok 4.5 loses more steps to execution than to reasoning. Its lower valid call rate, 94.1%, means roughly one call in seventeen is malformed, and in a ten step task that is a real probability of a wasted turn. It also takes more tool calls on average to solve the tasks it does solve, which suggests more exploratory or backtracking behavior. The upside of that behavior is speed and cost: each Grok call resolves faster and bills less, so even with more calls its tasks finish quicker in wall clock terms. Where it genuinely struggles is the long Level 3 chains, where the compounding cost of a slightly lower per step reliability shows up as a slightly lower completion rate.

Neither model is close to solving GAIA. Level 3 accuracy in the low forties means that on the hardest multi step tasks, both fail more often than they succeed, which is the honest state of general assistant agents in 2026 and a useful corrective to demos that only show the tasks that worked.

Reading the Trade

The composite read is that these are both capable agent models with different economics. Gemini 3.5 Pro is the reliability pick for an autonomous assistant running long unattended chains, where its higher valid call rate and fewer steps per task mean fewer opportunities for a run to derail, and where a failed multi step task costs more than the token savings of a cheaper model. Grok 4.5 is the value and latency pick for interactive assistants and shorter tasks, where a human is nearby to catch a stall, tasks rarely exceed a few steps, and the lower price plus faster turns compound across heavy daily use.

The way you run the agent matters as much as the model. An agent that validates tool calls before executing them, retries malformed calls cheaply, and caps its own step budget will narrow the reliability gap between these two, because much of Grok 4.5's disadvantage is execution slips a good harness can absorb. We saw the same pattern on tau-bench, where tool use reliability separated models more than raw capability did, and on BrowseComp, where web research depth was the deciding axis.

When This Applies to Your Stack

Pick by how the assistant will run, not by the headline accuracy. If you are deploying an agent that executes long chains without a human watching each step, weight the valid call rate and steps per task, because in a ten step task a small per step failure probability compounds into a real completion gap, and Gemini 3.5 Pro is the safer base there. If your assistant handles short interactive tasks with a person in the loop, Grok 4.5 gives you nearly the same accuracy at a lower price and faster turns, and its execution slips are the kind a human absorbs without noticing. Run GAIA style tasks drawn from your own workflows before committing, because your tool mix and task depth will move these numbers, and a public validation set cannot see the tools your assistant actually calls.

Choosing and integrating an agent model into a real assistant, with the tool call validation, retry handling, and step budgeting that keep an autonomous run from quietly stalling or burning tokens, is the AI integration and agent engineering work Contra Collective does. The benchmark tells you which model to start with; the engineering around it decides whether the assistant finishes real tasks or only the ones in the demo.

FAQ

What does GAIA actually measure? It measures whether a model can act as a general assistant on tasks that are simple for a person but require real actions: searching the web, reading files, doing arithmetic on what it finds, and chaining those steps to a single verifiable answer. Unlike one shot reasoning benchmarks it has no partial credit, so it rewards finishing the whole chain correctly, which is what an assistant has to do in practice.

Which model won overall? Gemini 3.5 Pro led on our run at 68.9% against Grok 4.5 at 65.4%, a gap under four points. The more decisive differences are Gemini 3.5 Pro's higher valid tool call rate and fewer steps per solved task, while Grok 4.5 is faster per task and cheaper per token, so the better choice depends on task length and cost sensitivity rather than the headline accuracy.

Why does the valid tool call rate matter so much? Because GAIA gives no partial credit and tasks run many steps, so every malformed call is a wasted turn and a chance for the plan to drift. A model that issues valid calls more reliably fails less often on long chains even at similar raw reasoning, which is why Gemini 3.5 Pro's lead widens as tasks get longer while the two are nearly tied on short ones.

Is Grok 4.5 a bad choice given the lower numbers? No. Grok 4.5 is the value and latency pick for interactive assistants and short tasks, where its lower price and faster turns compound across heavy use and a human catches the occasional stall. Its disadvantage is concentrated in long unattended chains, where a slightly lower per step reliability compounds; a good harness that validates and retries calls narrows much of that gap.

How should I use these numbers for my own project? As a starting shortlist, not a verdict. Weight the tools your assistant actually uses and how deep your tasks run, then run GAIA style tasks from your own workflows, because your tool mix will move the results. Prices listed are a snapshot and change frequently, so recheck current rates before committing to a model at volume.

This post discusses AI benchmark results and model selection for building assistants; it is general technical information, not a recommendation to rely on any model for high stakes decisions without your own testing.