Grok 4.5 vs Composer 2.5 in Cursor: Which Model Should You Use?

The decision is not “which model won?”

Cursor launched Grok 4.5 with SpaceXAI on July 8, 2026. Cursor describes Grok 4.5 as a jointly trained mixture-of-experts model for difficult long-running tasks across software engineering and broader knowledge work. In the same launch note, Cursor says Composer 2.5 remains available and describes it as a coding specialist. The company explicitly says the two models are different weight classes rather than one being a direct replacement for the other.

That distinction should shape the buying decision. If your workflow is mostly repository coding, refactoring, tests, and editor-native iteration, Composer 2.5 remains the obvious baseline. If the work crosses domains—code plus terminal tools, data analysis, web research, spreadsheets, financial modeling, or document work—Grok 4.5 is the more relevant first test.

The wrong question is “Which one has the higher benchmark score?” The better question is: which model completes my task with acceptable quality, review burden, and total cost?

What the official sources actually establish

Official sources: Cursor's Grok 4.5 launch note, SpaceXAI's Grok 4.5 release page, and Cursor's Composer 2.5 changelog.

The benchmark lesson is more important than the leaderboard

The launch materials are unusually useful because they show several benchmarks side by side. They also show why a single score is a weak buying signal.

The important point is not that one table is “right” and another is “wrong.” The point is that the harness is part of the result. DeepSWE's own v1.1 documentation says the update changed execution and grading while keeping the same long-horizon task set, with cleaner isolated grading and improved reproducibility.

For a buyer, this means a benchmark number should always be read together with the task, harness, scoring method, model configuration, and evaluation operator. Without those, the number is mostly a marketing object.

The contamination caveat is a trust signal, not a reason to ignore everything

Cursor disclosed that Grok 4.5 had an advantage on CursorBench because an earlier snapshot of the Cursor codebase was accidentally included in training. Cursor said the exact impact was unclear, removed that data for future models, and excluded CursorBench from the launch comparison.

That is narrower than saying the entire training corpus was “polluted.” The accurate buyer takeaway is:

• A benchmark can become unreliable when training data overlaps with evaluation material.
• The provider should disclose known overlap and avoid presenting the affected score as clean evidence.
• Buyers should reward clear disclosure, but still separate affected evidence from unaffected evaluations.

The practical lesson is not “never trust benchmarks.” It is “ask what evidence remains after compromised evidence is removed.”

Token price is not completed-task cost

At launch, Cursor and SpaceXAI listed the Grok 4.5 base model at $2 per million input tokens and $6 per million output tokens. Cursor also listed a faster variant at higher rates. SpaceXAI further claimed strong token efficiency, including lower average output-token use on its published SWE Bench Pro comparison.

Those are useful inputs, but they do not answer the buyer's real question. A model can be cheaper per token and still cost more per completed task if it needs extra retries, more correction rounds, or longer human review. A more expensive model can be cheaper overall if it finishes correctly in fewer steps.

For a real comparison, measure:

completed-task cost =
input-token cost
+ output-token cost
+ retries
+ correction rounds
+ review time
+ failed-run cleanup

This is the same reason AI coding subscriptions should not be compared only by monthly plan price. The bill is one layer; workflow friction is another.

A five-task pilot is more useful than another leaderboard screenshot

Use five tasks that represent your actual week. Do not cherry-pick tasks that flatter one model.

1. Repo edit: a multi-file feature or bug fix with tests.
2. Terminal task: investigate a failing service, package, or build workflow.
3. Data task: inspect a dataset, calculate something, and produce a checked result.
4. Research task: gather evidence from multiple sources and produce a decision memo.
5. Mixed task: combine code, tools, and documentation into one deliverable.

Run both models under the same conditions. Record completion, retries, test pass rate, accepted changes, cleanup time, and total usage. The winner for your team may be different by task type. That is not a problem; it is often the correct answer.

Decision matrix

Benchmark Hygiene Checklist

• [ ] Who ran the benchmark?
• [ ] What exact model configuration was used?
• [ ] What harness executed the model?
• [ ] Is the score provider-run, partner-run, or independently run?
• [ ] Does the task resemble your actual workflow?
• [ ] Are there known contamination or training-overlap caveats?
• [ ] Are competing models compared under equivalent conditions?
• [ ] Is token usage shown alongside success rate?
• [ ] Can you reproduce the comparison on a small internal task set?

So what

Grok 4.5 is interesting because it expands the model-choice problem inside Cursor. The old question was mostly which coding model handled a repository task best. The new question is broader: when work moves across code, terminals, data, research, and documents, should one general agentic model handle the whole chain, or should a coding specialist remain the default?

My answer is conservative: do not replace a working specialist with a broader model because of one launch chart. Make the broader model earn the switch on mixed tasks, and make the specialist defend its place on pure coding tasks.

The durable buyer rule is simple: model choice should follow task boundaries, evaluation quality, and completed-task cost. A leaderboard can suggest what to test. It cannot make the decision for you.

Related AI Picker notes

• AI Coding Budget Checklist — count usage, review time, context work, and switching friction.
• What an AI Coding Tool Really Costs After Week One — measure from the provider side and include retries.
• Evaluating the Real API Cost of a Switching LLM Harness — why routing and switching add hidden costs.
• AI Tool Trust Receipts — evidence to require before trusting a tool with code or workflow control.

Sources and further reading

• Cursor — Introducing Grok 4.5
• SpaceXAI — Introducing Grok 4.5
• Cursor — Composer 2.5 changelog
• Datacurve — DeepSWE v1.1 methodology note