Grok 4.5 lands with a story that is easy to misread. The headline is not “another frontier model” but “a coding model trained with your editor vendor in the loop, priced at $2/$6 per million tokens, and marketed on token efficiency rather than raw benchmark bragging.” If you run agentic coding in Cursor or Grok Build, the operational question is whether the ~2× token-efficiency claim and 80 TPS serving actually change your cost-per-task math, or whether you are still paying for long reasoning traces on hard SWE work.
What Grok 4.5 actually is
SpaceXAI’s default for coding, agents, and knowledge work · not a general chat refresh
SpaceXAI positions Grok 4.5 as its strongest model for real engineering: coding, multi-step agentic work, and knowledge tasks. The release is explicit that training happened alongside Cursor, which matters for harness fit even if it does not guarantee your repo’s edge cases are covered.
“Grok 4.5 is SpaceXAI’s smartest model built for coding, agentic tasks, and knowledge work.” – SpaceXAI, Introducing Grok 4.5
“Grok 4.5 was trained alongside Cursor.” – SpaceXAI, Introducing Grok 4.5
Merits of the argument. Co-training with a major coding harness is a credible signal for tool-use and edit-loop behaviour, not just leaderboard scores. SpaceXAI also publishes DeepSWE figures (66.1% pass@1 on DeepSWE 1.0 in their chart) with a note that competitor numbers come from developers’ own harnesses. Treat that as directional until you rerun on your stack.
Speed, token efficiency, and the real bill
80 TPS and fewer output tokens per SWE task · sticker price is only half the story
Two numbers drive operator economics here: throughput and tokens consumed per task. SpaceXAI claims fast-model serving at 80 tokens per second (TPS) and roughly double the token efficiency of comparable leading models on the same tasks, illustrated on SWE Bench Pro average output tokens.
“Grok 4.5 is served at fast-model speeds of 80 TPS.” – SpaceXAI, Introducing Grok 4.5
“Grok 4.5 is priced at $2 per million input tokens and $6 per million output tokens. The model also achieves roughly 2x the token efficiency of comparable leading models, solving tasks in under half the number of steps.” – SpaceXAI, Introducing Grok 4.5
Merits of the argument. Cost-per-task routing (see our Kimi K3 cost-per-task dossier) applies here: if output tokens halve on your SWE harness, effective output cost drops toward $3/M equivalent even at the listed $6/M rate. You still need to meter thinking traces, retries, and failed tool loops on your workloads.
| Metric | Grok 4.5 (official) | What to verify locally |
|---|---|---|
| Input price | $2 / 1M tokens | Prompt caching behaviour on your harness |
| Output price | $6 / 1M tokens | Mean output tokens per merged PR or ticket |
| Model ID | grok-4.5 |
Cursor plan vs SpaceXAI console vs Grok Build |
| Throughput claim | 80 TPS | p95 latency under parallel agent workers |
Where Grok 4.5 ships today
Grok Build, Cursor, and the SpaceXAI console · one model id across surfaces
Availability is intentionally broad: Grok Build as default, Cursor on all plans, and the SpaceXAI API with model id grok-4.5. SpaceXAI offered limited-time free usage in Grok Build and Cursor at launch; treat promos as time-boxed, not architectural.
“Grok 4.5 is available today in Grok Build, in Cursor on all plans, and from the SpaceXAI console.” – SpaceXAI, Introducing Grok 4.5
Merits of the argument. Same model id across editor and API reduces migration friction. The risk is organisational: teams standardise on Cursor’s Grok default without benchmarking against their existing router (Kimi K3, DeepSeek V4 Flash, Muse Spark, etc.) on ticket mix.
Failure cases that show up in production
When co-trained hype meets messy repos
- Benchmark harness ≠ your monorepo. DeepSWE gains may not transfer if your agent lacks the same tool budget, sandbox, or test oracle.
- Token efficiency claimed on SWE Bench Pro. Frontend polish, Excel modelling, or Office plugin workflows have different token profiles; do not extrapolate the 2× figure blindly.
- Free-period routing debt. Teams that default Grok 4.5 during a promo may forget to re-benchmark when billing normalises.
- Single-model Cursor default. Hard jobs still belong on a stronger reasoning tier if your eval shows regressions on security or multi-file refactors.
Migration and integration
Point API clients at grok-4.5 · keep harness instrumentation
For direct API use, SpaceXAI documents the Responses-style endpoint with model grok-4.5. In Cursor, select Grok 4.5 as the model and keep your existing agent rules (AGENTS.md, tool permissions). Log input/output tokens per task for two weeks before changing fleet-wide defaults.
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]}"
}'When Grok 4.5 is actually fine
High-volume coding agents with measurable token budgets
- Daily agentic coding where output token volume dominates cost and SWE-style tasks match the published efficiency story.
- Teams already on Cursor who want one fewer API vendor for the default model.
- Knowledge-work flows inside Grok Build (Excel, PowerPoint, Word plugins) where the product surface, not raw API, is the integration point.
What to check right now
Operator checklist before you flip the default
- Record baseline cost-per-merged-fix for your current default model (7-day sample).
- Run the same ticket sample on
grok-4.5with identical tool and timeout settings. - Compare mean output tokens, p95 latency, and pass rate on your internal eval set.
- Confirm billing path (Cursor included vs SpaceXAI API metered).
- Document fallback model for tasks where Grok 4.5 regresses (security review, rare languages).
Sources and references
Primary links for every quotation and load-bearing number in this dossier. Verify before you reuse a figure elsewhere.
- SpaceXAI – Introducing Grok 4.5 – pricing ($2/$6 per 1M), 80 TPS, token efficiency, Cursor co-training, availability, model id
grok-4.5 - sudoall.com – Kimi K3 cost-per-task routing – cost-per-task framing for agent fleets
nJoy 😉