Grok 4.5 is SpaceXAI’s coding and agentic AI model released July 8, 2026, jointly developed with Cursor on trillions of real developer interaction tokens. The model targets software engineers and enterprises running high-volume, multi-step AI workflows where cost per completed task determines production viability.
Grok 4.5 ranks 4th on the Artificial Analysis Intelligence Index while leading every frontier model on agentic tool-use. SpaceXAI prices it at $2/$6 per million tokens, delivers 2x token efficiency versus comparable models, and serves at 80 TPS. The Cursor partnership gives it a clear advantage on multi-file codebase navigation. Users report strong results on structured coding tasks but weaker performance on open-ended or creative work.
This review covers benchmark rankings, real-world coding performance, API pricing, access options, and hands-on user feedback. Whether Grok 4.5 fits a team’s stack depends on specific workflow types and volume. The sections ahead provide the data needed to make that call without relying on vendor claims.
What Is Grok 4.5?
Grok 4.5 is SpaceXAI’s flagship large language model released July 8, 2026, built for coding, agentic tasks, and knowledge work across software engineering, data science, finance, and legal applications. Here’s the thing: it’s not a chatbot upgrade. SpaceXAI positions Grok 4.5 as a direct peer to Claude, GPT, and Gemini for enterprise and developer production workloads where sustained multi-step performance matters.
The architecture’s a mixture-of-experts with a 500K token context window, equivalent to roughly 375,000 words. It supports text and image input with configurable reasoning effort at three levels: low, medium, and high.
Grok 4.5 Key Specs:
| Feature | Grok 4.5 |
|---|---|
| Architecture | Mixture-of-experts (MoE) |
| Context window | 500K tokens (~375,000 words) |
| Input modalities | Text and image |
| Reasoning effort | Low, medium, high (configurable) |
| Inference speed | 80 TPS (tokens per second) |
| EU availability | Not available (EU AI Act) |
So what does ‘long-running’ actually mean here? It means tasks that span hundreds of steps across hours of agent work. Not a single prompt-response. SpaceXAI designed Grok 4.5 for software engineering, data science, finance, and legal workflows that demand that kind of sustained focus.
Who Makes Grok 4.5 and Why Does It Matter?
SpaceXAI is the merged entity formed when xAI, Elon Musk’s AI research company, absorbed Cursor in early 2026, creating a combined organization with both frontier model infrastructure and real-world developer platform data. And here is the best part: that merger is what makes Grok 4.5 architecturally different from every other frontier model. No other lab had access to live developer behavioral data at this scale when training.
The model trained on Colossus infrastructure across tens of thousands of NVIDIA GB300 GPUs in Memphis. Asynchronous rollouts ran for many hours while training continued across the full cluster. That’s a serious compute commitment.
Earlier Grok models were general-purpose chatbots built around X activity data. Does that history matter for how you should evaluate Grok 4.5? Yes. It sets the baseline for how much the model has changed. Grok 4.5 is the first Grok release that SpaceXAI puts forward as a peer to models used in production coding and agentic workflows.
How Was Grok 4.5 Trained With Cursor?
Cursor contributed trillions of tokens of real developer-agent interaction data to Grok 4.5’s training, capturing not just code syntax but how developers actually navigate messy multi-file codebases, make refactoring decisions, and recover from failed tool calls. Think of it this way: every other frontier model was trained on code that looks correct. Grok 4.5 was trained on how developers actually work, which is a completely different signal.
Reinforcement learning ran across hundreds of thousands of multi-step software engineering tasks. The model learned to search repositories, use tools, carry implementations across files, test results, and retry after errors.
What Grok 4.5 Learned From RL Training:
- Search large repositories without losing task context
- Use tools and MCP servers without bloating the context window
- Carry implementations across multiple files in a single session
- Keep testing and code review in the loop during agent runs
- Recover from test failures and tool errors without restarting
- Spend fewer tokens on planning loops and repeated explanations
Does that training difference actually matter in practice? Yes. Grok 4.5 knows when to refactor versus when to leave code alone, a judgment no static training corpus can teach. That’s the competency the Cursor data unlocks.
How Does Grok 4.5 Perform on Benchmarks?
Grok 4.5 scores 54 on the Artificial Analysis Intelligence Index, placing 4th overall behind Fable 5, GPT-5.5, and Opus 4.8, while simultaneously achieving the outright best agentic tool-use score on the same board. In fact, that agentic tool-use result is the number worth sitting with. It’s not ‘top 4.’ It’s the outright best score across every model tested. Artificial Analysis calls it ‘amongst the leading models in intelligence and reasonably priced.’
Frontier Model Benchmark Comparison (Artificial Analysis Intelligence Index):
| Model | Rank | Index Score | Agentic Tool-Use |
|---|---|---|---|
| Fable 5 | 1st | Highest | Top tier |
| GPT-5.5 | 2nd | High | Top tier |
| Opus 4.8 | 3rd | High | Top tier |
| Grok 4.5 | 4th | 54 | #1 (best overall) |
On token efficiency, Grok 4.5 generated 60 million tokens on the full Intelligence Index run versus a 72-million-token class average. The model completes the same tasks with 17% fewer output tokens than the average competitor. And that efficiency gap compounds at scale.
SpaceXAI claims Grok 4.5 solves multistep tasks in under half the steps of comparable frontier models. The combination of 4th-place overall ranking and best-in-class agentic tool-use is a signal teams running multi-step agent loops should pay attention to.
How Does Grok 4.5 Compare to GPT-5.5?
GPT-5.5 ranks above Grok 4.5 on the Artificial Analysis Intelligence Index, with GPT-5.5 at 2nd place and Grok 4.5 at 4th, but the pricing gap between the two models is substantial enough to reframe the comparison entirely for most practical use cases. To be clear: Grok 4.5 sits at $2 per million input tokens. GPT-4.5-class models cost $15 to $30 per million input tokens. Does that pricing gap change the calculus for high-volume teams? Absolutely. That’s a 7x to 15x difference before efficiency is even factored in.
One independent reviewer ran Grok 4.5 through two website builds, a Go poker simulation, and a site audit. The reviewer found it felt ‘level with’ GPT-5.5 and Fable or ‘close enough I could not reliably tell them apart’ across those real tasks.
The SWE-bench multilingual score for GPT-5.5 comes from Cursor’s internal run rather than the vendor. Teams weighing cost against benchmark position will find Grok 4.5’s per-task economics strongest in high-volume code generation workflows where cost per run determines viability.
Is Grok 4.5 Better Than Claude Opus 4.8 for Coding?
No. Claude Opus 4.8 ranks 3rd on the Artificial Analysis Intelligence Index versus Grok 4.5’s 4th position, and Opus 4.8 delivers stronger benchmark performance across a broader range of task types beyond coding alone. For teams that need maximum benchmark performance across diverse workloads, Opus 4.8 holds the edge. But here’s the kicker: the price difference is significant, and so is the token efficiency gap.
Grok 4.5 is cheaper and faster than Opus 4.8. Elon Musk describes Grok as ‘more token-efficient and lower cost’ than Opus 4.7, and the API pricing reflects that directly. The $2/$6 per million token rates versus Opus 4.8’s higher tiers change the math for any team running at volume.
For agentic workflows where cost per completed task determines viability at scale, Grok 4.5’s 2x token efficiency can produce a better practical outcome than Opus 4.8 despite the lower benchmark ranking. Teams should model cost per task, not just cost per token.
What Are the Core Capabilities of Grok 4.5?
Grok 4.5 delivers three core capability areas: software engineering and coding, agentic task execution across multi-step workflows, and office and knowledge work including data science, financial modeling, and long-form document analysis. This isn’t a generalist model spread thin. Each area was targeted during training with dedicated datasets and reinforcement learning on domain-specific task categories.
The 500K token context window holds roughly 375,000 words. Engineering teams can load entire service boundaries or large codebases in one pass without chunking content across multiple API calls. That’s a real operational advantage for teams working on large repos.
Core Capability Areas:
- Software engineering: debugging, refactoring, multi-file codebase navigation
- Agentic task execution: multi-step workflows with tool use and error recovery
- Office and knowledge work: data science, finance, legal document analysis
- Code generation: Rust, C/C++, Python, JavaScript, and cross-language translation
- Structured data extraction: contracts, transcripts, support tickets, research papers
So what does 80 TPS actually mean for real work? It means the model is fast enough for interactive agent sessions where a two-second latency breaks the developer’s flow entirely. Speed is a feature at that level, not just a spec on a comparison chart.
Is Grok 4.5 Good for Agentic Coding?
Yes. Grok 4.5 achieves the outright best agentic tool-use score on the Artificial Analysis benchmark board, and SpaceXAI claims it solves multistep tasks in under half the steps of comparable frontier models, a metric that directly drives cost per completed agent task. For teams running agentic coding loops at volume, that step efficiency is the number that matters most. Fewer steps means fewer tokens billed and faster task completion per run.
The model was trained on hundreds of thousands of multi-step software engineering tasks through reinforcement learning. That training pattern mirrors what agent loops actually do: inspect a repository, plan changes, edit files, run commands, test results, and retry after errors.
Here’s why this matters for real agent workflows: Grok 4.5 uses tools and MCP servers without bloating the context window. The model keeps testing and review in the loop while spending fewer tokens on planning redundancy across long agent sessions.
Does Grok 4.5 Handle Multi-File Codebases?
Yes. Grok 4.5 was trained on Cursor’s real developer interaction data, which captures how developers navigate messy multi-file codebases, making it better at understanding project context than models trained solely on static code repositories. Syntactically correct code generation is a solved problem for any frontier model. The harder skill is understanding what a developer intends across a large codebase with legacy debt and mixed conventions. That’s where Grok 4.5’s training data creates a genuine edge.
The model is best suited for existing codebases: debugging, refactoring, and navigating unfamiliar code. One-shot generation from a blank slate isn’t where the training advantage concentrates.
Developer intent alignment is the specific competency SpaceXAI and Cursor targeted. The model learns when to refactor versus when to leave code alone, a judgment that requires long-context behavioral signal rather than pattern matching on code examples.
What Do Grok 4.5 Reviews Say?
Grok 4.5 reviews show a community divided roughly evenly: half of users rate it as genuinely excellent for cost-efficient coding work while the other half place it below Fable 5 and GPT-5.5 for demanding creative or high-stakes tasks. Here’s what that split actually tells you: the model performs best on structured, well-defined engineering work. Teams pushing it on creative or ambiguous tasks report more friction. Both camps are right because they’re testing different things.
One independent reviewer ran Grok 4.5 through website builds, a Go poker simulation, and a site audit. The reviewer called it ‘a genuinely good coding model now’ and ‘a game-changer for Grok.’ That verdict came after direct comparison against top-tier alternatives on the same tasks.
The same reviewer noted Grok 4.5 caught a bug that no other model flagged, including models rated higher overall. Should that single finding change how teams evaluate it? Not alone. But hands-on testing surfaces performance signals that aggregate benchmarks miss entirely.
What Are the Pros of Grok 4.5?
Grok 4.5 delivers three advantages that consistently appear across independent reviews: fast inference at 80 TPS, cost efficiency at $2/$6 per million tokens with 2x task efficiency, and strong multi-file codebase navigation from Cursor training data. The good news? Reviewers using Grok 4.5 for structured coding workflows consistently report it matches or approaches top-tier models in practice, even where benchmarks show a gap.
Pros:
- Fast inference at 80 TPS, suitable for interactive agent loops
- Low API cost: $2/M input, $6/M output (base tier)
- 2x token efficiency vs comparable frontier models
- Best-in-class agentic tool-use score on Artificial Analysis board
- Strong multi-file codebase navigation from real Cursor training data
- $0.31 per Intelligence Index task (5x cheaper than Claude Sonnet 5 max)
The cost advantage compounds with task efficiency. At $0.31 per Intelligence Index task, Grok 4.5 runs 5x cheaper than Claude Sonnet 5 max on the same benchmark. At high volume, that gap determines which models move from pilots into daily production use.
What Are the Common Complaints About Grok 4.5?
The most common complaint across Grok 4.5 reviews is that the model requires more specific guidance than top-tier alternatives, performing well with structured prompts but underdelivering on vague, open-ended, or ‘loaded’ instructions that other frontier models handle without clarification. The bad news? That guidance requirement is real and shows up across multiple independent testers. Is it a dealbreaker? Only if the team relies on open-ended prompting. For structured workflows, it’s a non-issue.
Cons:
- Needs more specific guidance than Fable 5 or GPT-5.5 on open-ended tasks
- Creative and 3D coding tasks underperform vs top-tier alternatives
- 500K context window carries a regression pattern from previous Grok versions
- High context cache hit cost in long sessions
- Not available in the EU (EU AI Act compliance)
- No locally deployable weights (proprietary model, no open release)
The 500K context window carries a known regression pattern from previous Grok models. High context cache hit cost and reliability drops in very long sessions are reported by users working at the upper end of that window. Worth testing at the context depth your workflows actually need before committing.
How Much Does Grok 4.5 Cost?
Grok 4.5 is priced at $2 per million input tokens and $6 per million output tokens for the base API, with a Fast variant at $4 per million input and $18 per million output for latency-critical applications requiring the highest throughput tier. The consumer SuperGrok subscription gives access at approximately $30 per month. One independent reviewer calls that ‘absurd value’ for the coding capability level it delivers, and it’s hard to argue otherwise at that price point.
The Intelligence Index task cost benchmark places Grok 4.5 at $0.31 per completed task. That’s roughly 5x cheaper than Claude Sonnet 5 max performing the same task. Bottom line: this is one of the most cost-efficient frontier models available for structured engineering work.
The 2x token efficiency claim means the effective output cost is lower than the per-token rate implies. SpaceXAI states Grok 4.5 delivers the highest intelligence per unit of time and cost among models in its tier. Independent benchmarks support that claim for agentic and coding workloads.
What Is the Grok 4.5 API Pricing?
The Grok 4.5 API runs two tiers: the base model at $2 per million input tokens and $6 per million output tokens, and the Fast variant at $4 per million input and $18 per million output for applications where speed is the primary constraint. Access it through the SpaceXAI console by setting the model name to ‘grok-4.5.’ Standard API features include function calling, structured outputs, web access, and streaming response.
Grok 4.5 Pricing Compared to Alternatives:
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Grok 4.5 (base) | $2 | $6 |
| Grok 4.5 (Fast) | $4 | $18 |
| GPT-4.5-class models | $15-$30 | Higher |
| GPT-4.5 (top tier) | $75 | $150 |
| SuperGrok (monthly) | ~$30/month (consumer) | |
The 2x token efficiency compounds the per-token savings. At $6 per million output tokens with 2x efficiency, the effective rate for completed tasks functions closer to $3 per million when compared to models that use twice the output tokens for the same task. That math changes the economics of high-volume workflows significantly.
Is Grok 4.5 Worth the Price?
Yes. Grok 4.5 ranks 4th on the Artificial Analysis Intelligence Index while delivering the most cost-efficient path to near-frontier performance available in 2026, making it the strongest value proposition for teams where cost per task determines production viability. SpaceXAI’s framing is direct: highest intelligence per unit of time and cost. Independent benchmarks support that claim for structured, high-volume workflows.
The model delivers the clearest value in code generation, refactoring, document summarization, and structured data extraction. These are high-volume, well-defined tasks where Grok 4.5’s cost efficiency matters most and its guidance requirement causes the least friction.
One reviewer put the tradeoff plainly: ‘Even though Fable is the better model, I am not paying Fable prices for the routine stuff, and this closes enough of the gap that I do not feel like I am compromising.’ That’s the honest framing for where Grok 4.5 earns its place in a model stack.
Where Can You Access and Use Grok 4.5?
Grok 4.5 is available across four primary surfaces: Grok Build, the Cursor editor on all subscription plans, the SpaceXAI API console, and the consumer-facing grok.com and X app for non-developer access. In fact, each surface targets a different user type. Grok Build serves engineering teams and CLI workflows. Cursor targets active developers. The API serves enterprise teams building custom pipelines. The consumer app serves individual users.
Cursor includes Grok 4.5 in its first-party model pool alongside Auto and Composer 2.5. Included usage is doubled through July 21, 2026 for individual and team plans as part of the launch period promotion. That’s a meaningful window to evaluate the model at reduced effective cost.
Where to Access Grok 4.5:
- Grok Build: SpaceXAI’s coding agent and CLI environment (default model)
- Cursor: all plans, desktop, web, iOS, and CLI (doubled usage through July 21, 2026)
- SpaceXAI API console: set model name to ‘grok-4.5’
- grok.com: consumer chat interface
- X app: integrated into the X platform for consumer access
- MindStudio and Kie.ai: third-party model aggregators
Grok 4.5 is not available in the EU under the EU AI Act. Teams in EU regions must use alternative models or route access through non-EU infrastructure. That’s a real constraint worth confirming before building any EU-facing workflows on the model.
Is Grok 4.5 Available Outside of Cursor?
Yes. Grok 4.5 is available outside Cursor through the SpaceXAI API, Grok Build, grok.com, the X app, and third-party model aggregators including MindStudio and Kie.ai, giving teams flexibility in how they incorporate it into existing workflows. Here’s what that means: the Cursor integration is the most prominent access route, but it’s not the only one. SpaceXAI designed multiple surfaces for different workflow types from the start.
Grok Build is SpaceXAI’s coding agent and command-line environment where Grok 4.5 is the default model. It’s capable of building complex Excel models with web research, multi-sheet formula use, and contextual annotations, which extends the model’s reach well beyond pure code.
API integration follows the standard pattern: create an API key in the SpaceXAI console, set the model name to ‘grok-4.5,’ and use function calling, structured outputs, web access, and streaming response. SDK documentation is available for teams building custom agents on top of the model.
Should You Trust Eat Proteins for AI Model Reviews?
You’re already here, so here’s our honest take. Our team at Eat Proteins applies the same evidence-based standard to AI model reviews that drives every supplement analysis: independent data, hands-on testing results, and cost-per-outcome over vendor claims and sponsored positioning. Grok 4.5 is a genuinely compelling model for structured coding and agentic workflows. It’s not a fix for every task, and we won’t pretend otherwise. But for the use cases where it excels, it’s the most cost-efficient near-frontier option available right now.
Don’t take SpaceXAI’s benchmark charts at face value. Our experts at Eat Proteins weight independent Artificial Analysis rankings, hands-on reviewer data, and real API pricing over anything a vendor produces about its own model. That’s the same standard applied to every protein product reviewed on this site.
The Grok 4.5 case is strong for teams where cost per completed task is the deciding factor. Is it the best model on the market? No. Is it the best value at near-frontier performance? The data says yes. And that’s a verdict our coaches at Eat Proteins are comfortable standing behind.
Why Does Eat Proteins Cover AI Model Reviews?
Eat Proteins expanded into AI model coverage because the audience that cares about optimizing protein intake and training science is the same audience that evaluates productivity tools rigorously, and both decisions respond to the same analytical framework of cost, evidence, and real-world results. The site covers the full performance stack: nutrition, training, and the AI tools that high-performing individuals use to operate more efficiently. That’s not a pivot. It’s a natural extension of the same methodology.
Our reviewers bring the same skepticism toward AI benchmark marketing that they apply to supplement label claims. Independent data gets weighted over vendor-produced numbers every time. That commitment doesn’t change when the product is a language model instead of a protein formula.
Readers who want evidence-based guidance on both protein optimization and AI productivity tools will find Eat Proteins a consistent source that doesn’t shift its standards between domains. The Grok 4.5 analysis reflects that commitment exactly.