Grok 4.5: Cursor's Co-Trained Coding Model, Explained

SpaceXAI and Cursor released Grok 4.5 on July 8, 2026 — the first model the two companies trained together, on trillions of tokens drawn from real Cursor coding sessions. The headline for developers: $2 per million input tokens, roughly twice the token efficiency of comparable flagship models on agentic benchmarks, and a training mix broadened beyond coding into general knowledge work. It is also the first model shipped under the SpaceXAI name, two days after xAI completed its rebrand following the February 2026 SpaceX merger; the Grok product name itself did not change.

Pricing and where it runs

Grok 4.5 ships in two variants. The base model costs $2 per million input tokens and $6 per million output tokens; a faster variant costs $4 input / $18 output per million tokens for latency-sensitive work. It is live today in Cursor across desktop, web, iOS, CLI and Cursor's SDK, plus in SpaceXAI's own Grok Build product, under the model ID grok-4.5. EU availability was not part of the initial rollout and is expected to follow in mid-July 2026.

Cursor is also running a promotion tied to the launch: individual and team subscribers get doubled usage for the first week.

The token-efficiency claim, and why it matters more than raw scores

SpaceXAI's most concrete developer-facing claim is efficiency, not accuracy. On SWE Bench Pro, Grok 4.5 averaged 15,954 output tokens per task versus 67,020 for Anthropic's Opus 4.8 in max-effort mode — about 4.2x fewer tokens for a comparable task, which SpaceXAI and Cursor describe as roughly double the token efficiency of leading models. Since agentic coding bills are dominated by output tokens spent on reasoning and tool calls, that ratio has more effect on your monthly bill than a few points of benchmark accuracy.

On raw resolve rates, Grok 4.5 does not lead the pack: it scored 64.7% on SWE Bench Pro and 83.3% on Terminal-Bench 2.1, against 80.4% and 84.3% respectively for Fable (max), the top scorer in the same comparison table. Treat all of these numbers as vendor-reported — they come from SpaceXAI and Cursor's own evaluation run, not an independent third party, and first-party benchmark tables have a well-documented habit of favoring their authors.

Benchmark	Grok 4.5	Comparison model
SWE Bench Pro (resolve rate)	64.7%	80.4% (Fable, max)
Terminal-Bench 2.1	83.3%	84.3% (Fable, max)
Output tokens on SWE Bench Pro (avg.)	15,954	67,020 (Opus 4.8, max)

Trained on Cursor sessions, not just code

The training story is the more interesting part for anyone building AI coding agents. SpaceXAI and Cursor built a distributed agent system to generate reinforcement-learning environments at scale, then trained Grok 4.5 with RL "on difficult problems in realistic environments spanning both software engineering and broader knowledge work." The training data itself is trillions of tokens of Cursor usage — real edits, tool calls and multi-step sessions, not just static code repositories.

That is a different bet than Cursor's prior model, Composer 2.5, which the companies trained specifically as a coding specialist. Grok 4.5 deliberately widens the mix to include STEM tasks and research papers, aiming at data science, finance and legal work in addition to software engineering. SpaceXAI and Cursor describe the two models as different weight classes rather than a straight upgrade path, and Composer 2.5 remains available alongside the new release.

What changes for developers this week

• If you already use Cursor: Grok 4.5 is a model picker choice, not a migration — try it on your existing agent tasks and compare token spend against whatever you use today, especially on long, multi-step sessions where the efficiency claim should show up directly in cost.
• If you're pricing out coding agents: at $2/$6 per million tokens, Grok 4.5 undercuts most flagship-tier pricing (compare it against the tiered rollout in OpenAI's GPT-5.6 family), so it is worth a place in any tier-routing setup even if you keep another model as the default.
• If you're outside the EU rollout window: confirm current availability before planning a switch — the mid-July EU date was a stated expectation, not a guarantee, at launch.
• Either way, verify benchmarks yourself. Run your own eval suite before trusting the resolve-rate or token-efficiency numbers for your specific codebase; vendor benchmarks measure the vendor's chosen tasks, not yours.

Grok 4.5 arrived the same week as OpenAI's GPT-5.6 family and Meta's first paid model API, in what is shaping up as the most price-competitive stretch of releases this year. For developers, the practical takeaway is the same across all three: run your own evals, and let per-task cost — not the launch blog post — decide which model does the work.