When xAI launched Grok 4.5 on July 8, 2026, the headline numbers told one story: 83.3% on Terminal-Bench 2.1, effectively tied with GPT-5.5 at 83.4%. Frontier-level performance, another contender in the top tier.

But dig into the cost and efficiency numbers and a second, arguably more interesting story emerges — one about what happens when a model is co-trained with real agentic task data from the ground up.

The Model

Grok 4.5 is a 1.5-trillion-parameter Mixture-of-Experts (MoE) architecture — roughly three times the scale of Grok 4.3, though parameter counts for MoE models are notoriously hard to compare directly since only a fraction of parameters activate on any given token. The “V9” architecture, as some internal documents have reportedly labeled it, was co-developed in collaboration with Cursor, trained on real developer interaction data from actual agentic coding sessions.

This co-training approach is significant. Most frontier models are trained primarily on internet-scale text with some RLHF fine-tuning. Grok 4.5 was trained with coding agent workflows baked in from the start — the model has seen what real multi-step code generation, debugging, and testing loops look like as a first-class training signal, not an afterthought.

The Benchmark Numbers

On Terminal-Bench 2.1, the benchmark specifically designed to evaluate models on terminal-based agentic tasks:

  • Grok 4.5: 83.3%
  • GPT-5.5: 83.4% (virtual tie)
  • Fable 5: 84.3% (current leader)
  • Opus 4.8: 78.9%

In standard coding benchmarks like SWE-Bench Pro, Grok 4.5 trails some competitors — it’s not the dominant choice for every task type. But for terminal-centric agentic workflows specifically, it’s competitive with the best models available.

The Efficiency Story

Here’s where Grok 4.5 genuinely differentiates. Artificial Analysis’ Coding Agent Index measured $2.49 average cost per agentic task for Grok 4.5 in the Grok Build environment. For comparison:

  • GPT-5.5 in Codex: ~$5.07 per task
  • Fable 5 in Claude Code: ~$11.80 per task

Grok 4.5 delivers near-equivalent terminal-bench performance at roughly half the cost of GPT-5.5 and less than a quarter the cost of Fable 5.

The efficiency advantage comes from two sources. First, the raw token pricing: $2/M input tokens (with $0.50/M for cached tokens) and $6/M output tokens is competitive at the frontier tier. Second — and this is the more interesting story — Grok 4.5 uses approximately 1.9M tokens per task on average versus 6–7M+ for competitors. It reaches correct solutions with fewer reasoning and retry steps.

Whether this is a genuine efficiency gain from the Cursor co-training (the model learned how to code effectively on real workflows) or partially an artifact of benchmark characteristics is worth watching. But at the task-cost level that Artificial Analysis measures, the advantage is real and substantial.

API Access and Context Window

Grok 4.5 is available via:

  • Grok Build CLI (xAI’s native coding agent interface)
  • SpaceXAI API (model name: grok-4.5)
  • Cursor (as the default model following the co-training partnership)

Context window is 500K tokens — shorter than some frontier competitors but sufficient for most agentic coding tasks.

EU availability was projected for mid-July and may be live or imminent by the time you read this.

What This Means for OpenClaw Users

For OpenClaw users who access models via OpenRouter or direct API, Grok 4.5 is a compelling option to evaluate — particularly for agentic coding tasks where you’re watching token costs at scale.

The $2.49 average task cost is specifically measured in a Grok Build context, and your numbers will vary depending on your task structure, system prompt size, and how much context you’re passing. But even accounting for variation, the token pricing alone makes it worth a test run against whatever frontier model you’re currently using for coding workflows.

The 500K context window is the main constraint to evaluate against your use case — if you’re regularly working with very large codebases, you’ll want to compare against models with longer native context.

Sources

  1. Grok 4.5 — x.ai/news
  2. ExplainX.ai — Grok 4.5 Public Launch, SpaceXAI July 2026
  3. Latent Space — AI News: SpaceXAI launches Grok 4.5
  4. Artificial Analysis — Grok 4.5 Intelligence Index

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260715-2000

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