
Grok 4.7 소개
장시간 실행되는 코딩과 지식 노동에 가장 뛰어난 성능을 발휘하는 모델입니다.
A frontier model for long-running coding and knowledge work, at the same price and speed as Grok 4.6.

장시간 실행되는 코딩과 지식 노동에 가장 뛰어난 성능을 발휘하는 모델입니다.

Grok 4.7의 공식 모델 카드입니다. 모델의 성능 벤치마크와 안전장치 평가 결과를 담고 있습니다.

Cursor는 모델 학습을 가속하기 위해 SpaceX와 협력합니다.
Grok 4.7 stays with difficult tasks across many steps. It uses tools, checks its own work, adjusts its approach, and keeps moving toward a finished result.
Grok 4.7 works across large codebases and extended engineering projects. It researches unfamiliar systems, edits across files, runs tests, and verifies the result.
Beyond code, Grok 4.7 works across documents, spreadsheets, presentations, and other professional artifacts. Give it a multi-step project that requires research, analysis, and synthesis.
Grok 4.7 verifies results more carefully than Grok 4.6 and manages longer context, with a 256k standard window and 500k long context.
Grok 4.7 is built for long-running coding and knowledge work. It uses a larger base than Grok 4.6 and a longer training run on harder, multi-hour tasks. It handles projects that span research, analysis, implementation, and several rounds of refinement.
On CursorBench 4.0 it scores 46.3% at extra high effort. It is served at the same price and speed as Grok 4.6, and Cursor subscription plans for individuals and teams include significant usage of the model.
We trained Grok 4.7 jointly with SpaceXAI on a new, larger base model than Grok 4.6. The reinforcement learning run was longer and weighted toward problems that take many hours to complete.
The model is better at verifying its own work and managing longer context. It was also trained to understand the Grok Bot harness, which improves conversational tasks and general knowledge work.
| Grok 4.7 xHigh | Grok 4.6 High | GPT-5.6 Sol Max | Fable 5.1 Max | |
|---|---|---|---|---|
| CursorBench 4.0Software engineering | 46.3% | 40.4% | 41.7% | 51.8% |
| DeepSWE v1.1Software engineering | 71.0% high effort | 65.2% | 72.7% | 70.0% |
| EEBenchElectrical engineering | 64.0% | 53.0% | 39.4% | 56.4% |
| AA Briefcase v1.1Multi-hour office work | 1,657 | 1,546 | 1,487 | 1,678 |
| Terminal-Bench 4.0Multi-hour terminal work | 37.6% | 20.3% | 37.3% | 57.9% |
| Harvey Legal Agent BenchmarkLegal work | 19.6% | 15.8% | 2.5% | 6.7% |
| HealthBench ProfessionalClinical reasoning | 56.7% | 48.5% | 60.5% | 62.1% |
Grok 4.7 발표 자료에 나온 점수입니다. Grok 4.7의 DeepSWE 결과는 높은 추론 수준에서 측정한 것입니다.
GDPval에서 Grok 4.7은 매우 높은 추론 수준으로 1,695 Elo를 기록했습니다. Grok 4.6은 1,605, Fable 5.1은 1,735, GPT-6 Astra는 1,542입니다.
(Above) Grok 4.7 results across agentic coding and knowledge work benchmarks.
Grok 4.7 is available today in Cursor across desktop, web, iOS, CLI, and our SDK.

A frontier model for long-running coding and knowledge work, at the same price and speed as Grok 4.6.

장시간 실행되는 코딩과 지식 노동에 가장 뛰어난 성능을 발휘하는 모델입니다.

Grok 4.7의 공식 모델 카드입니다. 모델의 성능 벤치마크와 안전장치 평가 결과를 담고 있습니다.

Cursor는 모델 학습을 가속하기 위해 SpaceX와 협력합니다.
Grok 4.7 stays with difficult tasks across many steps. It uses tools, checks its own work, adjusts its approach, and keeps moving toward a finished result.
Grok 4.7 works across large codebases and extended engineering projects. It researches unfamiliar systems, edits across files, runs tests, and verifies the result.
Beyond code, Grok 4.7 works across documents, spreadsheets, presentations, and other professional artifacts. Give it a multi-step project that requires research, analysis, and synthesis.
Grok 4.7 verifies results more carefully than Grok 4.6 and manages longer context, with a 256k standard window and 500k long context.
Grok 4.7 is built for long-running coding and knowledge work. It uses a larger base than Grok 4.6 and a longer training run on harder, multi-hour tasks. It handles projects that span research, analysis, implementation, and several rounds of refinement.
On CursorBench 4.0 it scores 46.3% at extra high effort. It is served at the same price and speed as Grok 4.6, and Cursor subscription plans for individuals and teams include significant usage of the model.
We trained Grok 4.7 jointly with SpaceXAI on a new, larger base model than Grok 4.6. The reinforcement learning run was longer and weighted toward problems that take many hours to complete.
The model is better at verifying its own work and managing longer context. It was also trained to understand the Grok Bot harness, which improves conversational tasks and general knowledge work.
| Grok 4.7 xHigh | Grok 4.6 High | GPT-5.6 Sol Max | Fable 5.1 Max | |
|---|---|---|---|---|
| CursorBench 4.0Software engineering | 46.3% | 40.4% | 41.7% | 51.8% |
| DeepSWE v1.1Software engineering | 71.0% high effort | 65.2% | 72.7% | 70.0% |
| EEBenchElectrical engineering | 64.0% | 53.0% | 39.4% | 56.4% |
| AA Briefcase v1.1Multi-hour office work | 1,657 | 1,546 | 1,487 | 1,678 |
| Terminal-Bench 4.0Multi-hour terminal work | 37.6% | 20.3% | 37.3% | 57.9% |
| Harvey Legal Agent BenchmarkLegal work | 19.6% | 15.8% | 2.5% | 6.7% |
| HealthBench ProfessionalClinical reasoning | 56.7% | 48.5% | 60.5% | 62.1% |
Grok 4.7 발표 자료에 나온 점수입니다. Grok 4.7의 DeepSWE 결과는 높은 추론 수준에서 측정한 것입니다.
GDPval에서 Grok 4.7은 매우 높은 추론 수준으로 1,695 Elo를 기록했습니다. Grok 4.6은 1,605, Fable 5.1은 1,735, GPT-6 Astra는 1,542입니다.
(Above) Grok 4.7 results across agentic coding and knowledge work benchmarks.
Grok 4.7 is available today in Cursor across desktop, web, iOS, CLI, and our SDK.

(Above) Grok 4.7 scores 46.3% at extra high effort on CursorBench 4.0.
| Model | Score | CostCost / task | TokensTokens / task | StepsSteps / task |
|---|---|---|---|---|
| Grok 4.7 Extra High | 46.3% | $6.01 | 70,141 | 88 |
(Above) Grok 4.7 scores 46.3% at extra high effort on CursorBench 4.0.
| Model | Score | CostCost / task | TokensTokens / task | StepsSteps / task |
|---|---|---|---|---|
| Grok 4.7 Extra High | 46.3% | $6.01 | 70,141 | 88 |