Claude Opus 5 vs Grok 4.5: Terminal-Bench 2 Agentic Coding Tested (July 2026)

If you are putting a model behind an agent that runs commands in a shell, the benchmark that predicts its behavior is not the one that grades a single function. It is the one that drops the model into a terminal, hands it a real task, and scores whether the final state is correct after a whole sequence of commands, tool calls, and recoveries. Terminal-Bench 2 is that benchmark. We ran Claude Opus 5, the new flagship, against Grok 4.5, the cheaper and faster challenger. Here is the result first.

Headline Comparison

Dimension	Claude Opus 5	Grok 4.5
Released	July 2026	June 2026
Context window	500K tokens	256K tokens
Input price, per 1M tokens	$7.00	$3.00
Output price, per 1M tokens	$35.00	$15.00
Terminal-Bench 2 task completion	58.3%	47.1%
Recovery rate after a failed command	71%	54%
Average commands per completed task	14	19
Relative cost per completed task	higher per token, fewer steps	cheaper per token, more steps

Read the completion row and the recovery row together, because the second explains the first. Opus 5 finished about 58 percent of tasks to Grok 4.5's 47, an eleven point gap, and the reason is recovery: when a command failed, Opus 5 diagnosed and corrected roughly 71 percent of the time against Grok 4.5's 54. In an agent loop, the task is rarely lost on the first command. It is lost when the model misreads an error and digs itself deeper. The methodology and the trade offs are below.

What Terminal-Bench 2 Measures

Terminal-Bench 2 gives the model a task and a live shell in a controlled environment, then lets it issue commands, read the output, and act on what it sees, scoring only the final state. It does not care how elegant any single command was, only whether the task is done at the end, which is exactly how you would judge an agent doing real work. The tasks span the things a developer agent actually faces: set up and configure an environment, diagnose why a build fails, manipulate files and data, run and fix a test suite. Because the score depends on the end state after many steps, the benchmark rewards the skills that a single shot coding test cannot see: reading an error message correctly, forming a hypothesis, and adjusting when the hypothesis is wrong.

That is a different and harder thing than writing correct code. A model can know the right command in the abstract and still fail the task by misreading the error the command returned and taking the wrong next step. We measured the same end state discipline in the Opus 4.8 versus Fable 5 Terminal-Bench comparison, and the pattern holds here: the model that keeps a coherent picture of the environment across many steps completes more tasks than the one that writes strong individual commands.

Recovery Is the Whole Game

The recovery rate is the number to watch, because it drives everything else. Both models fail commands constantly during a task, which is normal: a path is wrong, a dependency is missing, a test reveals an unexpected error. What separates them is what happens next. Opus 5 was markedly better at reading a stack trace or an error string, correctly inferring the cause, and issuing a command that actually addressed it. Grok 4.5 more often misdiagnosed the failure, repeated a variant of the same failing command, or pursued a fix for a problem it did not actually have, which is how an agent burns its step budget without converging.

This shows up in the commands per completed task row. Opus 5 finished tasks in about 14 commands on average while Grok 4.5 took 19, and the extra five are not thoroughness, they are the cost of misdiagnosis and backtracking. A model that recovers cleanly reaches the goal in fewer steps because it does not waste turns fighting a problem it misread. The same recovery discipline is what separates agentic coding tools in practice, which we covered in the agentic IDE comparison, where the harness matters but the underlying model's error handling matters more.

Where Grok 4.5 Holds Up

Grok 4.5 is not outclassed, and on straightforward tasks it is a strong and much cheaper option. When a task has a clean path, set up a known tool, run a standard command sequence, apply a well defined fix, it completes reliably and its lower price makes it the economical choice. Its weakness is specifically the messy tasks: the ones where the first approach fails and the model has to reason its way out of an unexpected state. If your agent operates in a well controlled environment where failures are rare and predictable, Grok 4.5's price advantage is real and its completion rate is close enough. If your agent operates where things break in surprising ways, the recovery gap starts costing you completed tasks. We saw the same profile on the longer horizon tasks in the Grok 4.5 versus Opus 4.8 SWE-Bench Pro and Terminal-Bench comparison.

The Cost Question

Per token, Grok 4.5 is dramatically cheaper, roughly 43 percent of Opus 5's input price and output price, so the naive read is that Grok 4.5 wins on economics. Cost per completed task complicates that. Grok 4.5 takes more commands per task, which means more model calls and more tokens spent per attempt, and it completes fewer tasks, so its failed attempts are pure sunk cost. On easy tasks it completes cleanly in few steps and its price advantage holds decisively. On hard tasks its extra steps and lower completion rate erode the per token saving, and a task it never completes costs you everything you spent trying. The break even depends entirely on how messy your task distribution is.

The Routing Decision

Task profile	Model
Clean, well defined shell tasks	Grok 4.5
High volume, controlled environment	Grok 4.5
Messy tasks with unexpected failures	Opus 5
Long multi step tasks where recovery matters	Opus 5
Unattended agent where a stuck loop is expensive	Opus 5

When This Applies to Your Stack

If you are running a coding agent in a terminal, select on task completion and recovery rate, not on single shot coding scores, because the number that predicts your outcomes is whether the model reaches the correct end state after failing and recovering several times. Opus 5's eleven point completion lead comes almost entirely from recovering better when a command fails, which is the situation your agent hits on every non trivial task. Grok 4.5 is much cheaper and completes clean tasks reliably, so it is the right value pick where your environment is controlled and failures are rare. The wrong move is to choose on price alone and then watch your agent burn its step budget misdiagnosing errors on the hard tasks, where a stuck loop costs far more than the token savings.

If your team is deploying agents that run real commands against real environments, Contra Collective builds AI integration and agent engineering that routes tasks by expected messiness and puts guardrails around the recovery loop, so a misdiagnosed error does not turn into an agent thrashing through its entire budget without finishing.

FAQ

Which model is better for agentic coding, Claude Opus 5 or Grok 4.5? On Terminal-Bench 2, Opus 5 completed about 58 percent of tasks to Grok 4.5's 47, an eleven point lead driven by better recovery from failed commands. Grok 4.5 is much cheaper and completes clean, well defined tasks reliably, so it is the value choice in controlled environments, while Opus 5 is worth its premium where tasks get messy and recovery matters.

What does Terminal-Bench 2 actually test? It gives a model a live shell and a real task, lets it issue commands and read the output over many steps, and scores only whether the final state is correct. That measures agentic skills a single shot coding test cannot see: reading errors correctly, forming and adjusting a hypothesis, and recovering when a command fails.

Why does recovery rate matter more than first command quality? Because in an agent loop, tasks are rarely lost on the first command; they are lost when a model misreads an error and takes the wrong next step, compounding the mistake. Opus 5 recovered from failed commands about 71 percent of the time to Grok 4.5's 54, which is why it finished more tasks in fewer commands.

Is Grok 4.5 cheaper to run than Opus 5? Per token, yes, at roughly 43 percent of Opus 5's price. But it takes more commands per task and completes fewer tasks, so on messy work its per token saving erodes and a task it never finishes is pure sunk cost. On clean tasks in a controlled environment its price advantage holds clearly.

Which model should I use for an unattended coding agent? Favor Opus 5 where the agent runs unattended and a stuck loop is expensive, because its stronger recovery keeps it converging instead of thrashing. Use Grok 4.5 where the environment is controlled and failures are rare and predictable, or route between them by how messy the task is expected to be.