99.9% SLA
Grok 4.5: Model Introduction & Practical Guide
Grok 4.5 is SpaceXAI's (formerly xAI's) flagship large language model, built on a 1.5-trillion-parameter V9 architecture and specifically sharpened for software engineering through deep integration of Cursor coding data during supplementary training. Elon Musk positioned it as an "Opus 4.7-class" model - and the pricing tells you the real strategy: $2/$6 per million tokens, a fraction of what comparable flagships charge.
Here's the short version: Grok 4.5 is the value play among frontier models. It ranks fourth globally on GDPval-AA v2, scores 83.3% on Terminal-Bench 2.1 (ahead of Claude Opus 4.8's 78.9%), generates at 80 tokens per second, and - the headline number for cost-conscious teams - completes SWE-Bench Pro tasks in about 15,954 tokens, roughly one-quarter of what top competitors consume. Its single-task cost on GDPval is $0.49, about 90% cheaper than the models ranked above it. The trade-offs: it trails on multilingual software engineering (78.0% vs Opus 4.8's 84.4%), gives up some ground on the hardest SWE-Bench Pro tasks, and its ecosystem is younger than Anthropic's or OpenAI's.
This guide covers model overview, core features, technical specifications, capability comparison, core advantages, recommended use cases, example prompts, and selection recommendations.
Quick Facts
| Attribute | Value |
|---|---|
| Model name | Grok 4.5 |
| Developer | SpaceXAI (formerly xAI) |
| Category | Flagship large language model |
| Architecture | 1.5T-parameter V9 base, MoE |
| Output speed | 80 tokens/s |
| Context window | Large (documented for long-horizon engineering) |
| Key benchmarks | Terminal-Bench 2.1 83.3% · DeepSWE 1.0 62.0% · SWE-Bench Pro 64.7% · GDPval-AA v2 Elo 1543 (4th globally) |
| Token efficiency | ~15,954 tokens per SWE-Bench Pro task (~1/4 of top competitors) |
| Task cost | $0.49 per GDPval-AA v2 task (~90% below top-3 models) |
| API pricing | $2 / 1M input · $6 / 1M output (OpenAI-compatible) |
| Access | Grok Build platform, Cursor IDE (cursor-grok-4.5), SpaceXAI console API |
| Training | Tens of thousands of NVIDIA GB300 GPUs; RL over hundreds of thousands of multi-step engineering tasks |
Table of Contents
- Model Overview
- Core Features
- Technical Specifications
- Capability Comparison
- Core Advantages
- Recommended Use Cases
- Example Prompts
- Selection Recommendations
- FAQ
- Sources & Further Reading
1. Model Overview
SpaceXAI's Grok 4.5 marks the company's shift from "fast-talking chatbot" to "serious engineering model." The architecture is a 1.5T-parameter V9 base trained on tens of thousands of GB300 GPUs, but the more consequential choice is the data: supplementary training incorporated Cursor programming data, directly targeting the workflows where coding models earn their keep - completion, bug fixing, refactoring, and automated test generation.
The reinforcement learning stage followed the same theme: hundreds of thousands of multi-step software engineering tasks with agentic asynchronous training, designed to improve performance on long-horizon work where the model must plan, execute, verify, and recover. The result profile is unusual among flagships - it optimizes token efficiency as a first-class metric. On SWE-Bench Pro tasks, Grok 4.5 averages 15,954 tokens versus 67,020 for Claude Opus 4.8, a 4.2x reduction that translates directly into lower bills at equal task completion.
Where it lands in the pecking order is best described as "frontier-adjacent with best-in-class economics." The model ranks fourth globally on GDPval-AA v2 (Elo 1543), with a per-task cost of $0.49 - roughly 90% below the models ranked above it, and below cheaper rivals like GLM-5.2 and Kimi K2.6. For teams running coding agents at volume, those two numbers matter more than a few points of benchmark spread.
Distribution covers the three surfaces developers actually use: Grok Build (the conversational platform), Cursor (the IDE, as cursor-grok-4.5), and the SpaceXAI API with OpenAI-compatible endpoints.
2. Core Features
Cursor-hardened code generation. Deep training on Cursor data for completion, bug repair, project refactoring, and automated test generation - tuned for the everyday reality of IDE work, not just competitive programming.
Multi-step engineering tasks. Cross-file dependency analysis, architecture design, and technical documentation generation, with strength in long-horizon software processes.
Asynchronous agent execution. Supports long-running agent tasks with complex multi-turn decisions and office automation flows.
80 tokens/s output. Fast enough for real-time interaction and long agent sessions without the "thinking for minutes" experience of slower flagships.
Multilingual programming. Strong cross-language understanding and conversion (SWE-Bench Multilingual 78.0%).
Native IDE and platform integration. Runs inside Cursor and Grok Build - no environment switching, no wrapper tooling.
OpenAI-compatible API. Standard interface for enterprise integration and batch calls through the SpaceXAI console.
3. Technical Specifications
| Specification | Detail |
|---|---|
| Architecture | V9 base, 1.5T parameters (MoE) |
| Training infrastructure | Tens of thousands of NVIDIA GB300 GPUs |
| Supplementary training | Cursor programming data; quality-scored, domain-filtered corpora |
| RL scope | Hundreds of thousands of multi-step software engineering tasks, async agentic training |
| Output speed | 80 tokens/s |
| Token consumption | ~15,954 tokens per SWE-Bench Pro task |
| API pricing | $2 / 1M input · $6 / 1M output |
Published benchmarks:
| Benchmark | Grok 4.5 | Claude Opus 4.8 |
|---|---|---|
| Terminal-Bench 2.1 | 83.3% | 78.9% |
| SWE-Bench Multilingual | 78.0% | 84.4% |
| DeepSWE 1.0 | 62.0% (high) | 55.8% (max) |
| SWE-Bench Pro | 64.7% (high) | 69.2% (max) |
| GDPval-AA v2 Elo | 1543 (4th globally) | 1600 (top 3) |
| Task cost (GDPval) | $0.49 | ~$5-10 |
| Tokens per SWE-Bench Pro task | 15,954 | 67,020 |
| Output speed | 80 tok/s | Slower |
4. Capability Comparison
Positioning summary. Grok 4.5 wins on three axes simultaneously - price ($2/$6 per million tokens), speed (80 tok/s), and token efficiency (4.2x fewer tokens per task) - while staying within a few points of the frontier on most engineering benchmarks. It loses on multilingual software engineering and the hardest SWE-Bench Pro tasks, where Claude Opus 4.8's maximum-effort runs pull ahead. The strategic read: for teams whose bottleneck is cost per completed task rather than peak capability on any single task, Grok 4.5 is currently the strongest value in the frontier tier.
5. Core Advantages
- Frontier-adjacent at one-fifth the cost. $2/$6 per million tokens versus $10/$50 for premium competitors.
- 4.2x token efficiency. Fewer tokens per completed task compounds into dramatically lower real spend - and faster task completion.
- 80 tokens/s output. Interactive-grade speed from a flagship model.
- Engineering-first data. Cursor training data and RL over real multi-step tasks produce practical coding competence.
- Zero-friction integration. Cursor and Grok Build support plus an OpenAI-compatible API.
- Terminal strength. 83.3% on Terminal-Bench 2.1 - the top published score against its nearest competitor.
6. Recommended Use Cases
- IDE-assisted development: code completion, bug diagnosis, refactoring, and test generation inside Cursor.
- Multi-step engineering workflows: cross-file dependency analysis, architecture design, and documentation generation.
- High-volume coding agents: cost-per-task economics make large fleets viable.
- Enterprise API integration: customer service bots, data analysis, and workflow automation at low marginal cost.
- Terminal and ops assistance: script generation, command queries, and configuration generation for ops engineers and data scientists.
7. Example Prompts
1. IDE refactoring task
2. Long-task agent run
3. Multi-file dependency analysis
4. Multilingual conversion
5. API integration scaffold
8. Selection Recommendations
Choose Grok 4.5 if:
- Cost per completed task dominates your model selection.
- You run coding agents at volume in Cursor or similar IDEs.
- You need fast output for interactive and long-running sessions.
- Terminal-centric and multi-step engineering tasks are your primary workload.
- You want OpenAI-compatible integration without bespoke tooling.
Consider alternatives if:
- You need the best multilingual software engineering performance (Claude Opus 4.8 leads at 84.4%).
- The hardest SWE-Bench Pro tasks are your benchmark - max-effort competitors pull ahead.
- You require the largest published blind-test knowledge-work scores (GDPval top-3).
- Your procurement requires specific regional compliance frameworks.
Sources & Further Reading
Benchmark and pricing figures are as published at review time; SpaceXAI's model lineup and rate cards change frequently. Validate task-level costs on your own workflows - token efficiency gains depend on task type.
