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SpaceXAI Releases Grok 4.5, a Cursor-Trained Model for Coding, Agentic Tasks, and Knowledge Work at $2/M Input

SpaceXAI released Grok 4.5, a general-purpose AI model trained alongside Cursor for coding, agentic tasks, and knowledge work, priced at $2 per million input tokens. The model claims superior token efficiency and ranks first on Harvey's Legal Agent Benchmark, though it trails competitor Fable on several coding benchmarks.

read5 min views1 publishedJul 8, 2026
SpaceXAI Releases Grok 4.5, a Cursor-Trained Model for Coding, Agentic Tasks, and Knowledge Work at $2/M Input
Image: MarkTechPost

SpaceXAI just released ** Grok 4.5**. The company calls it its smartest model to date. It targets coding, agentic tasks, and knowledge work. SpaceXAI says Grok 4.5 was trained alongside Cursor, an AI coding editor.

TL;DR

  • Grok 4.5 targets coding, agentic tasks, and knowledge work, trained alongside Cursor.
  • In SpaceXAI’s own chart, Fable (max) leads all four benchmarks; Grok 4.5 is closest on Terminal Bench 2.1.
  • Token efficiency is the standout: about 4.2× fewer output tokens than Opus 4.8 (max) on SWE Bench Pro.
  • Pricing is $2/M input and $6/M output, served at 80 TPS.
  • It ranks #1 on Harvey’s Legal Agent Benchmark and is the default model in Grok Build.

What is Grok 4.5?

Grok 4.5 is a general-purpose model tuned for real engineering work. SpaceXAI trained it on datasets spanning coding, science, engineering, and math. The research team describes its reasoning as both intelligent and efficient. It scored #1 on Harvey’s Legal Agent Benchmark, which SpaceXAI cites as office-work strength.

How SpaceXAI trained Grok 4.5?

Training ran across tens of thousands of NVIDIA GB300 GPUs. SpaceXAI used training and stability techniques designed for large-scale runs. Beyond raw token volume, the team invested in data filtering and curation. This included deduplication, quality scoring, and domain-focused selection.

SpaceXAI team then scaled reinforcement learning with a focus on per-token intelligence. RL covered hundreds of thousands of tasks. Most centered on multi-step software engineering and other technical work. Grading combined automated and model-based methods. The stack supports highly asynchronous training. Agentic rollouts can run for many hours while learning continues.

Benchmark performance

SpaceXAI team published scores across four coding benchmarks. Competitor figures come from published system cards or leaderboards. SpaceXAI’s prose says Grok 4.5 exceeds comparable leading models. Its own chart is more mixed. Fable (max) posts the top score on all four benchmarks. Grok 4.5 stays closest on Terminal Bench 2.1.

Quick reference: “pass@1” counts only first-attempt passes; “resolve rate” is the share of tasks fixed.

Benchmark (harness) Grok 4.5 Top listed Others
DeepSWE 1.0 — pass@1 (each provider’s harness) 62.0% Fable (max) 66.1% GPT 5.5 (xhigh) 64.31%; Opus 4.8 (max) 55.75%
DeepSWE 1.1 (mini-swe-agent harness, DataCurve) 53% Fable (max) 70% GPT 5.5 (xhigh) 67%; Opus 4.8 (max) 59%; GLM 5.2 44%
Terminal Bench 2.1 83.3% Fable (max) 84.3% GPT 5.5 (xhigh) 83.4%; Opus 4.8 (max) 78.9%
SWE Bench Pro — resolve rate 64.7% Fable (max) 80.4% Opus 4.8 (max) 69.2%; GLM 5.2 62.1%; GPT 5.5 (xhigh) 58.6%

Speed and token efficiency

Grok 4.5 is served at 80 TPS. SpaceXAI reports roughly twice the token efficiency of leading models. On SWE Bench Pro, Grok 4.5 resolved tasks with 15,954 output tokens on average. SpaceXAI reports Opus 4.8 (max) used 67,020 on the same benchmark. That is about 4.2× fewer output tokens. Fewer output tokens usually means lower output cost and latency per task.

Pricing

Grok 4.5 costs $2 per million input tokens and $6 per million output tokens. SpaceXAI says it solves tasks in under half the number of steps. Confirm current pricing in the SpaceXAI console before budgeting.

Use cases with examples

Codebase repair: find a bug, fix it, then explain the root cause.App prototyping: build a Three.js solar-system simulation from one prompt.Legal agent tasks: Grok 4.5 ranks #1 on Harvey’s Legal Agent Benchmark.Spreadsheet work: build multi-sheet Excel models that pull in web research.Documentation: turn an outline into slides and a Word report.

Getting started (working code)

Grok 4.5 is available in Grok Build, in Cursor on all plans, and from the SpaceXAI console. Grab an API key and call the responses endpoint. The model ID is grok-4.5

.

curl -s https://api.x.ai/v1/responses \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "grok-4.5",
    "input": "Find and fix the bug, then explain it: function median(a){a.sort();return a[a.length/2]}"
  }'

To use Grok Build from the terminal, install the CLI:

curl -fsSL https://x.ai/cli/install.sh | bash

Grok 4.5 at a glance #

Attribute Detail
Vendor SpaceXAI
Focus Coding, agentic tasks, knowledge work
Training partner Cursor
Hardware Tens of thousands of NVIDIA GB300 GPUs
Serving speed 80 TPS
Token efficiency ~4.2× fewer output tokens than Opus 4.8 (max) on SWE Bench Pro
Input price $2 / million tokens
Output price $6 / million tokens
Access Grok Build, Cursor (all plans), SpaceXAI console
Model ID grok-4.5

Availability and limits

Grok 4.5 is live in Grok Build and in Cursor on all plans. It is also available via the SpaceXAI console. It is not yet available in the EU. SpaceXAI expects EU availability in mid-July. Free usage is offered for a limited time in Grok Build and Cursor.

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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.

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