介绍 Grok 4.7
我们最强大的模型，专为长时间运行的编程与知识工作打造。
Grok 4.7
A frontier model for long-running coding and knowledge work, at the same price and speed as Grok 4.6.
Announcements
Grok 4.7 模型卡
Grok 4.7 官方模型卡，收录了该模型的能力基准测试结果和安全防护评估。
Cursor 与 SpaceX 合作推进模型训练
Cursor 正在与 SpaceX 合作，加速我们的模型训练。
Built for coding and knowledge work alike
Long-horizon problemsLong-horizon
problems
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.
Ambitious engineeringAmbitious
engineering
Grok 4.7 works across large codebases and extended engineering projects. It researches unfamiliar systems, edits across files, runs tests, and verifies the result.
In-depth knowledge workIn-depth
knowledge work
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.
Checks its own workChecks its
own work
Grok 4.7 verifies results more carefully than Grok 4.6 and manages longer context, with a 256k standard window and 500k long context.
Built for more than software engineering
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.
A strong foundation
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.
Benchmarks
| 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% |
得分来自 Grok 4.7 发布公告。Grok 4.7 的 DeepSWE 成绩是在高投入级别下取得的。
在 GDPval 上，Grok 4.7 以超高投入级别取得 1,695 Elo 分；Grok 4.6、Fable 5.1 和 GPT-6 Astra 则分别取得 1,605、1,735 和 1,542 Elo 分。
(Above) Grok 4.7 results across agentic coding and knowledge work benchmarks.
Available everywhere you work
Grok 4.7 is available today in Cursor across desktop, web, iOS, CLI, and our SDK.
Interactive demo with multiple windows showing Cursor's AI-powered features. The interface is displayed over a subtle, solid brand background.
Set up Datadog APM for all API routes
Explored 12 files, 3 searches
I'll add the Datadog SDK, instrument all API routes with tracing, and set up error tracking.
Worked for 7m 42s
Processed screen recording
Done — here's the Datadog dashboard showing all instrumented routes.
Acme Labs
Summary
Integrated Datadog APM across all 24 API routes with error tracking and custom metrics.
Add a follow up...
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FAQ
Grok 4.7 is a frontier model from Cursor and SpaceXAI for coding and knowledge work. It builds on Grok 4.6 with a larger base, longer training on harder multi-hour tasks, and stronger self-verification.
Grok 4.7 is available today in Cursor across desktop, web, iOS, CLI, and our SDK. You can also build your own agents on top of Grok 4.7 with our SDK docs.
Grok 4.7 is part of the Cursor Models pool on individual and team plans, alongside Grok 4.6, Grok 4.5, and Composer 2.5. It is priced the same as Grok 4.6. Standard on-demand usage is $2/M input, $0.50/M cached input, and $6/M output tokens. The Fast variant is $4/M input, $1/M cached input, and $12/M output tokens. See the model docs for full details.