Grok 4.7 Explained: xAI Architecture, CursorBench Results and Pricing
The most interesting number in the Grok 4.7 launch is not a benchmark score. It is $6.01. On CursorBench 4.0 at the highest effort setting, Grok 4.7 reportedly scores 46.3 percent at about six dollars per task, while Claude Opus 5 scores 46.6 percent at about twelve. Near-identical accuracy at roughly half the bill is a very different pitch from the one xAI made a year ago, when the argument was raw capability.
It matters now because coding agents have turned model choice into a unit-economics decision. A model that is cheap per token but burns two or three times the tokens per task is not cheap, and a model that tops a vendor chart can fall well behind on a neutral harness. This post sorts what xAI actually disclosed from what outlets and Elon Musk have reported, works through the cost arithmetic, and says where the model fits and where it does not.
What this covers: release facts and lineage, the disclosed and undisclosed architecture, the training claims, every benchmark we could source with its caveats, the two-tier pricing and caching mechanics, availability, failure modes, a comparison matrix, and a practical adoption checklist.
Context and Background
Grok is the model family from xAI, which some coverage now refers to as SpaceXAI following the corporate combination with SpaceX; the product name and API remain Grok. The family has moved quickly. Our earlier Grok 4.5 deep-dive covered the generation before the 4.6 point release, and Grok 4.7 is the third increment in that line, announced on 21 September 2026 on xAI’s own news page, Introducing Grok 4.7.
One correction up front, because several aggregator posts circulated with later dates: the primary announcement is dated 21 September, and the commentary pieces that picked it up a few days later (including the weekly roundup that prompted this article) are secondary. Where this post gives a date, it is the xAI date.
The competitive context matters more than the version number. By autumn 2026 the frontier is crowded: Anthropic’s Claude Opus 5 and its successors, OpenAI’s GPT-5.6 and GPT-6 line, and Google’s Gemini 3.5 and 4 families all target agentic coding and knowledge work. We have covered several of them, including Claude Opus 5.5, Claude Sonnet 5.5 and GPT-6 Sol and Luna. In that field, xAI has staked out the position of “near-frontier at a fraction of the price”, and Grok 4.7 is the clearest expression of it so far.
Distribution is the other half of the story. Grok 4.7 launched on day one inside Cursor, the xAI API, Grok Build (xAI’s coding harness), and GitHub Copilot, plus model routers and several clouds. For a coding model, being the default option inside the editor where developers already work is worth more than a two-point benchmark lead. If you are choosing among agentic editors rather than models, our comparison of Cursor, Windsurf and Claude Code covers the harness side of the decision.
Finally, a word on sourcing. xAI’s page discloses scores and prices but, per our reading and per every secondary summary we checked, does not disclose the context window, parameter count, architecture, or API model identifiers. Those details appear elsewhere as reported figures. This article labels each one accordingly, because the gap between “disclosed” and “reported” is exactly where model write-ups usually go wrong.
What Grok 4.7 Is: Verified Facts and Reported Specs
The short answer: Grok 4.7 is xAI’s September 2026 flagship text-and-code model, positioned for coding and knowledge work. xAI says it uses a larger base model than Grok 4.6 with extended reinforcement learning. Standard API pricing is $2 per million input tokens and $6 per million output tokens. A 500K-token context window is reported by third parties, not stated by xAI.
Figure 1: Grok 4.7 at a glance, separating what xAI states from what is reported. The base model and RL stage are disclosed in outline; the parameter count and context window come from secondary sources.
The diagram shows the path from a larger pretrained base, through reinforcement learning weighted toward long tasks, to a model served in two speed variants and two price tiers. Read it as a map of claims, not a blueprint: the arrows between boxes are xAI’s description, while the contents of each box are mostly undisclosed.
Specification table with provenance
| Attribute | Value | Source status |
|---|---|---|
| Announced | 21 September 2026 | xAI news page |
| Standard price | $2 input, $6 output per 1M tokens | xAI news page |
| Fast variant | Twice the output speed at twice the price ($4 / $12) | xAI states the ratio; $4 / $12 per secondary coverage |
| Cached input | $0.50 per 1M tokens under 200K prompt | Reported by secondary outlets |
| Long-context tier | $4 input, $12 output, $1 cached above 200K tokens | Reported by secondary outlets |
| Context window | 500,000 tokens, unchanged from 4.6 | Reported, not on xAI page |
| Base model | “Larger” than Grok 4.6 | xAI states |
| Parameter count | 2.1 trillion | Attributed to Musk by one outlet; not in the announcement |
| Modalities | Text focused; no multimodal claims in the announcement | xAI page |
| Batch API | Not supported | Reported by one outlet |
| Chat app and X | Not at launch, “at a later date” | Reported by one outlet |
Treat the 2.1 trillion figure with care. It is a total parameter count attributed to a public statement by Musk and relayed by one outlet. xAI’s announcement does not repeat it, does not say whether the model is dense or Mixture-of-Experts (MoE), and gives no active-parameter figure. For a sparse model the total count alone says little about inference cost, which is why we do not use it in any calculation below. Our primer on Mixture-of-Experts architecture explains why total and active parameters diverge.
What a “larger base model” implies, and what it does not
xAI’s phrasing is that Grok 4.7 builds on a larger base model than Grok 4.6 and then applies more reinforcement learning. That is a two-lever story. Scaling the base improves the prior: factual coverage, code idiom knowledge, tolerance for ambiguous instructions. Extending the reinforcement learning stage improves behaviour under a verifiable reward: the agent loop of editing, running tests, reading failures, and trying again.
What it does not imply is a clean attribution. When CursorBench rises from 40.4 to 46.3 percent between 4.6 and 4.7, no public ablation says how much came from the bigger base and how much from longer RL. Anyone who tells you “it is mostly the RL” is guessing. The honest framing is that both levers moved at once, and the post-training stack is where xAI says most of the agentic gain lives.
Effort levels as a product surface
Grok 4.7 exposes effort settings (reported as low, medium, high and xhigh), and xAI’s note, as relayed by Lindy, is that the spacing between them was widened so the settings behave more differently from each other than in 4.6. This is important for reading benchmarks: the 46.3 percent CursorBench figure is at the top effort setting, and the DeepSWE figure is quoted at high. Comparing a vendor’s top-effort score against a competitor’s default-effort score is the single most common way benchmark tables mislead. Our write-up of reasoning effort control and thinking budgets covers how these dials work in serving stacks.
Training and Post-Training: What xAI Says and What It Does Not
xAI discloses the shape of the training recipe and none of the quantities. Nothing public gives the pretraining token count, the compute budget, the data mix, or the reward design. What the announcement and secondary coverage do describe is a set of behavioural targets, and those are worth reading closely because they tell you what the reward signal was probably built around.
Figure 2: The Grok 4.7 post-training pipeline as described by xAI, with undisclosed quantities noted in the text. Reinforcement learning on multi-hour tasks is the stage the announcement emphasises.
The figure traces a larger base model into extended reinforcement learning on long-horizon tasks, followed by a safeguard stack, then release as standard and fast variants. The quantities at each stage are not published, so the diagram records order and purpose only.
Reinforcement learning weighted toward long tasks
Lindy’s summary of the announcement says the reinforcement learning was weighted toward multi-hour tasks and that long-context reasoning improved. xAI’s own text emphasises better context management and self-verification. Put together, the target behaviour is an agent that keeps a coherent plan across hundreds of tool calls, notices when its own patch did not fix the failing test, and does not lose the thread when the transcript gets long.
The plausible mechanism is a reward computed from an outcome check, such as hidden tests passing, applied across entire trajectories rather than single turns. That is an inference from the stated emphasis, not a disclosed method. Verifiable-reward training is the common pattern across the 2026 frontier, and it has a known side effect that shows up in the numbers below: models trained to keep trying burn more tokens.
The token appetite
The most useful single fact in the Lindy write-up is the reported output volume: roughly 81,000 tokens per task at high effort for Grok 4.7, against roughly 36,000 for Grok 4.6, an increase of about 125 percent. That figure is a reported measurement, not an xAI-published one, and we cannot reproduce it. But it is consistent with the direction of the training emphasis, and it changes the cost story.
Illustrative arithmetic, using only the reported per-task output volumes and the $6 per million output price (input and cached costs excluded, so this understates total cost): 81,000 tokens at $6 per million is about $0.49 of output per task, against about $0.22 for 4.6’s 36,000 tokens if priced identically. Same price card, more than double the output spend. Price per token held flat while price per task rose, which is the thing to watch in every agent-era model release.
Safeguards trained in, not bolted on
xAI describes an all-new safeguard stack and calls Grok 4.7 the strongest model it has tested on refusals. The quantitative claim, repeated by several outlets, is a 3.3 percent pass-through rate on HackerBench v0.3 for high-risk dual-use prompts, meaning the model allowed that fraction of prompts it was supposed to block. xAI also cites a biosafety score of 62.4 percent on LatchBio. We cannot assess how those benchmarks were built, and both are vendor-reported. The more useful reading is directional: the company is now shipping safety numbers alongside capability numbers, which was not true of earlier Grok launches.
Benchmarks: What Is Reported, By Whom, and How Far to Trust It
The first rule of reading this table is to ask who ran each test. Nearly every number below comes from xAI’s own announcement. The exceptions come from Artificial Analysis, an independent evaluator, and they are systematically lower.
Figure 3: How to read the Grok 4.7 benchmark claims, sorting vendor-reported results from independently measured ones. The gap between the two on Terminal-Bench is the key caveat.
The figure groups results by evaluator rather than by topic. Vendor-run results sit on one side and independent measurements on the other, with the Terminal-Bench discrepancy marked because it is the largest single disagreement in the data.
xAI-reported results, Grok 4.7 versus Grok 4.6
| Benchmark | Grok 4.7 | Grok 4.6 | Change |
|---|---|---|---|
| CursorBench 4.0 (xhigh) | 46.3% | 40.4% | +5.9 points |
| DeepSWE v1.1 (high) | 71.0% | 65.2% | +5.8 points |
| EEBench | 64.0% | 53.0% | +11.0 points |
| AA Briefcase v1.1 | 1,657 | 1,546 | +111 |
| Terminal-Bench 4.0 | 37.6% | 20.3% | +17.3 points |
| Harvey Legal Agent | 19.6% | 15.8% | +3.8 points |
| HealthBench Professional | 56.7% | 48.5% | +8.2 points |
The pattern is consistent: broad improvement, with the biggest relative jump on Terminal-Bench, where the score nearly doubled. Note that one outlet quotes Terminal-Bench as 38.0 percent and others as 37.6 percent; the xAI page figure we sourced is 37.6, and the difference is probably rounding or a harness revision. We use 37.6.
The independent view
Artificial Analysis, as relayed by two outlets, gives Grok 4.7 an Intelligence Index score of 46, against 53 for the leading Claude and GPT models named in that coverage. More pointedly, on a neutral Terminal-Bench 4.0 harness, Artificial Analysis reportedly measured 26 percent for Grok 4.7, against 55 percent for Anthropic’s top model in the same comparison. The vendor-reported 37.6 and the independent 26 are not necessarily in conflict; they were probably run with different scaffolding, tool configurations and time limits. But they illustrate the central problem with agentic benchmarks: the harness is part of the result.
Lindy also reports a Terminal-Bench figure of 66.4 percent for Claude Opus 5.5, which would put Grok 4.7 far behind on long autonomous terminal tasks. We have not independently confirmed that number and the harness behind it may differ from xAI’s, so read it as a signal about direction (Anthropic leads on long-horizon terminal work) rather than a precise gap.
CursorBench, the headline, in context
CursorBench is a benchmark built by the Cursor team from real engineering tasks inside their own editor, which makes it closer to daily work than synthetic puzzle sets. At the highest effort setting, the cost-per-task comparison reported by Tech Insider is Grok 4.7 at 46.3 percent for $6.01 per task and Claude Opus 5 at 46.6 percent for $11.95 per task. We could not retrieve Cursor’s own methodology page, so the per-task dollar figures should be treated as reported.
Illustrative arithmetic on those two reported pairs: dividing cost by accuracy gives roughly $12.98 per successfully solved task for Grok 4.7 ($6.01 / 0.463) and roughly $25.64 for Opus 5 ($11.95 / 0.466). That is a ratio near 2.0. The calculation assumes failed attempts are not retried and that the task mix is representative, so it is a comparison of expected spend, not a quote for your workload.
Methodology caveats worth stating plainly
- Top-effort scores are not default-effort scores. If your product runs at medium, expect lower numbers and lower cost.
- Benchmark versions move. CursorBench 4.0, DeepSWE v1.1 and Terminal-Bench 4.0 are version-pinned; comparisons with older versions are invalid.
- Contamination cannot be ruled out for any public benchmark, and xAI does not publish a decontamination analysis.
- Several of the named comparison models appear in the sources under product names and prices we have not independently verified, such as the Claude and GPT price points quoted in secondary coverage. We use those prices only in clearly labelled illustrative calculations.
Pricing, Caching and Access: The Economics of a Token-Hungry Model
Per-token price is the number on the pricing page, but cost per task is the number on the invoice. Grok 4.7 is cheap on the first and, depending on effort level, merely competitive on the second. The pricing structure also has two features that reward careful engineering: a prompt cache and a long-context surcharge.
Figure 4: How a Grok 4.7 request is priced depending on prompt length and cache state, and where the model can be reached. Tier boundaries and cached rates are as reported by secondary outlets.
The figure shows a request splitting at the 200K-token prompt boundary. Below it sits the base rate with a discounted cached rate; above it sits a doubled rate applied to the whole request. Access channels fan out beneath, from first-party endpoints to third-party clouds and editors.
The price card
| Tier | Input | Cached input | Output |
|---|---|---|---|
| Standard, prompts under 200K | $2.00 | $0.50 | $6.00 |
| Long context, 200K and above | $4.00 | $1.00 | $12.00 |
| Fast variant | $4.00 | not reported | $12.00 |
All prices are per million tokens. The standard row for input and output comes from xAI’s announcement; the cached and long-context rows, and the explicit dollar figures for the fast variant, come from secondary coverage (Tech Insider, Lindy and Xenospectrum agree with each other on them). xAI itself states the fast variant is twice the output speed at twice the price, which is consistent with $4 and $12. Verify against the live price page before budgeting; xAI has changed price cards between releases before.
Two details from the secondary reporting deserve attention. First, the long-context surcharge reportedly applies to the entire request once the prompt crosses 200K tokens, not only to the tokens beyond the threshold. Second, a Batch API with deferred-job discounts is reportedly not supported at launch. For offline workloads such as nightly repository analysis, that removes a discount that rival providers offer.
Worked example: caching in an agent loop
Illustrative arithmetic, not a measurement. Suppose a coding agent holds a 100,000-token repository context and makes 30 model calls in a session, each re-sending that context plus small additions (ignored here for simplicity).
- Without caching: 30 calls multiplied by 100,000 tokens is 3.0 million input tokens, at $2 per million, so $6.00.
- With caching, assuming the first call is a full-price miss and the other 29 are cache hits: 100,000 tokens at $2 per million is $0.20, plus 2.9 million tokens at $0.50 per million is $1.45, for $1.65 total.
That is a 72 percent reduction in input cost for the same session. The savings depend entirely on cache hit behaviour, which depends on keeping the prefix byte-stable between calls. Reordering tool definitions, inserting a timestamp near the top of the prompt, or editing an earlier message invalidates the prefix. Our deeper treatment of prompt caching architecture and economics walks through the failure patterns.
Worked example: the long-context cliff
Again illustrative. A single request with a 250,000-token prompt and a 5,000-token answer would fall in the long-context tier: 250,000 tokens at $4 per million is $1.00, plus 5,000 tokens at $12 per million is $0.06, so $1.06. If you trimmed the same prompt to 190,000 tokens and stayed in the standard tier, the input would cost $0.38 and the 5,000-token answer $0.03, for $0.41. A 24 percent cut in tokens produced a 61 percent cut in price, because of the whole-request pricing. Applications that hover near the 200K line should treat it as a hard budget, and context-engineering work (summarising, pruning, retrieving) pays for itself quickly there.
Where to reach it
Per xAI and secondary reports, Grok 4.7 is available in the xAI API, Grok Build, Cursor, GitHub Copilot across Pro, Pro+, Max, Business and Enterprise plans (with pay-as-you-go billing for the premium model usage), OpenRouter, Vercel, Cloudflare, Oracle Cloud and Amazon Bedrock. The fast variant is reported to be limited to Cursor and Grok Build. The Grok consumer app and the X platform were not part of the launch. There are no open weights, and xAI discloses no self-hosting path, so questions about VRAM requirements and quantization do not apply; this is an API-only model.
For teams running multiple vendors behind a router, day-one availability on Bedrock, OpenRouter and Vercel lowers the cost of trying it. We discuss how routing policies decide between cheap and expensive models in LLM model routing architecture. Grok 4.7 is an obvious candidate for the tier that handles medium-difficulty code edits, with a more expensive model reserved for the long autonomous tasks where it reportedly trails.
How Grok 4.7 Compares
The comparison below uses only reported figures and the prices quoted in the cited coverage. Because rival model names and prices come from the same secondary articles, treat the matrix as a decision aid for scoping a pilot, not a procurement document.
| Dimension | Grok 4.7 | Claude Opus 5 / 5.5 class | GPT-5.6 Sol class |
|---|---|---|---|
| Reported API price per 1M tokens | $2 in, $6 out | Premium tier, reported at several times higher | Reported $4 in, $20 out |
| CursorBench 4.0, top effort | 46.3% at $6.01 per task | 46.6% at $11.95 per task (Opus 5) | Not reported in our sources |
| Long autonomous terminal tasks | Weaker, 26% independent, 37.6% vendor | Reported stronger, 66.4% reported for Opus 5.5 | Not reported in our sources |
| Context window | 500K reported | Varies by model, check vendor | Varies by model, check vendor |
| Open weights | No | No | No |
| Batch discount | Not at launch, reported | Offered by Anthropic historically, verify | Offered by OpenAI historically, verify |
Which model for which job
For interactive pair-programming inside an editor, where tasks are minutes long and a human reviews the diff, Grok 4.7 looks strong on value. The CursorBench result is the closest published proxy for that workload, and the cost per task is lowest among the reported pair.
For long autonomous runs, such as a multi-hour migration without human checkpoints, the independent Terminal-Bench evidence points the other way. A model that fails a quarter or more of tasks silently costs more in review and rework than the token savings recover.
For regulated knowledge work, the Harvey Legal Agent score of 19.6 percent is a reminder that none of these models is close to unsupervised on hard professional tasks. We would not read the 3.8-point move over 4.6 as a deployment threshold in any direction.
For security-sensitive environments, the prompt injection exposure of any tool-using agent matters more than the benchmark. Our analysis of agentic AI security and prompt injection applies to Grok 4.7 as much as to any peer, whatever its refusal numbers say.
Trade-offs, Gotchas, and What Goes Wrong
A cheap model with a high ceiling still has sharp edges. These are the ones that will matter in production, ordered roughly by how often they bite.
Token appetite eats the price advantage. If the reported 81K-versus-36K output comparison holds in your workload, a flat per-token price means roughly double the output spend per task against Grok 4.6, before counting that higher effort settings also read more context in each turn. The fix is to measure tokens per resolved task on your own repositories at each effort level and choose the lowest setting that clears your acceptance bar. Do not carry over a vendor’s top-effort configuration into production by default.
Harness dependence. The gap between the vendor’s 37.6 percent and the independent 26 percent on Terminal-Bench is a warning that scaffolding changes results by double digits. If you swap the model inside your own agent loop, you are measuring a new combination of prompt, tool schema, retry policy and model. Run a held-out evaluation set of your own tickets before changing defaults. Our guide to LLM evaluation with judge pipelines covers building one that does not drift.
The 200K cliff. Because the reported surcharge applies to the whole request, prompts that creep from 190K to 210K tokens double their input cost overnight. Agents that append tool output without pruning will cross it mid-session. Add a context budget with a hard stop and summarisation before the threshold, not after.
No batch discount. Offline jobs pay the interactive price. If a large share of your spend is nightly or backfill work, compare against vendors that discount deferred execution before concluding that $2 and $6 is the cheapest option.
Long-horizon reliability. The reported weakness is long autonomous work, where an early wrong turn compounds. Symptoms to watch for are patches that satisfy the visible failing test while breaking an adjacent behaviour, claims of success without running the suite, and plans abandoned silently after context grows. xAI emphasises self-verification as an improvement, but the independent numbers suggest it is not yet a substitute for external checks. Gate any autonomous merge on tests the agent cannot edit.
Dual-use and refusal calibration. The 3.3 percent pass-through figure is encouraging for abuse resistance, but low pass-through can come with over-refusal, which hurts security research and red-team workflows. xAI says it tuned to maintain usability for legitimate security work; the evidence for that is its own report. If your use case touches offensive-security tooling, test refusals against your actual prompts.
Concentration and vendor risk. Day-one availability across many clouds and editors reduces access risk, but the model itself is single-vendor with no open weights. A pricing change or deprecation lands on you with no fallback except another vendor. Keep a router in front of it so you can switch.
Unverified pieces. The parameter count, the context window and the Batch API status all come from secondary reporting. If any of them drive an architectural decision, confirm against xAI’s documentation first.
Practical Recommendations
Treat Grok 4.7 as a strong default for human-in-the-loop coding at a low price, and as a candidate for the mid tier of a routed system. Do not treat it as a drop-in replacement for the most capable model on long autonomous work, because the independent evidence does not support that.
The adoption path that has worked for us with new models is short and boring. Run it before you change any default.
- Build a 30 to 50 ticket evaluation set from your own repositories, with tests the agent cannot modify.
- Run Grok 4.7 at medium, high and xhigh effort in your actual harness, and record resolved rate, output tokens and total dollars per task.
- Compute cost per solved task, not cost per task, using the same arithmetic as the CursorBench example above.
- Measure prompt-cache hit rate. If it is below roughly 80 percent, fix prompt prefix stability before comparing prices.
- Set a context budget below 200K tokens and test the summarisation path.
- Keep a router and a fallback vendor. Route long autonomous jobs to whichever model scores best on your own terminal-style tasks.
- Re-run the evaluation when xAI ships a point release; between 4.6 and 4.7 the output-token profile changed by more than the price did.
If your workload is mostly short edits and code explanation, the likely result is a meaningful saving with little quality loss. If it is multi-hour autonomous refactoring, expect to keep a pricier model on that path and use Grok 4.7 for everything around it.
Frequently Asked Questions
What is Grok 4.7?
Grok 4.7 is xAI’s flagship large language model, announced on 21 September 2026 and aimed at coding agents and knowledge work. xAI says it is built on a larger base model than Grok 4.6 with extended reinforcement learning, better context management and self-verification, and a new safeguard stack. Pricing starts at $2 per million input tokens and $6 per million output tokens. It is text focused and API only.
How much does Grok 4.7 cost?
The announced standard price is $2 per million input tokens and $6 per million output tokens. Secondary outlets report $0.50 per million cached input tokens, and a long-context tier above 200K-token prompts at $4 input, $1 cached and $12 output applied to the whole request. A fast variant costs twice as much for twice the output speed. Check xAI’s live price page before budgeting, since cached and tier rates are not in the announcement text we reviewed.
What is Grok 4.7’s CursorBench score?
xAI reports 46.3 percent on CursorBench 4.0 at the highest effort setting, up from 40.4 percent for Grok 4.6. Tech Insider reports Claude Opus 5 at 46.6 percent on the same benchmark, at $11.95 per task against $6.01 for Grok 4.7. These are vendor and press figures; the per-task dollar numbers could not be checked against Cursor’s own methodology, so treat them as reported.
Does Grok 4.7 have a 500K context window?
Several outlets report a 500,000-token context window, unchanged from Grok 4.6, but the xAI announcement does not state a context window and we could not confirm it from a primary source. Requests above 200K tokens reportedly move to a higher-priced tier applied to the entire request. If your design depends on the full window, test it with your own long documents and confirm in xAI’s API documentation.
Is Grok 4.7 better than Claude Opus 5 for coding?
It depends on the task. On CursorBench at top effort the two are within 0.3 points of each other, with Grok 4.7 reported at about half the cost per task. On long autonomous terminal work, independent testing reported by outlets shows Grok 4.7 well behind, at 26 percent on a neutral Terminal-Bench harness. For editor-based work with human review Grok 4.7 looks competitive; for unattended multi-hour runs, the evidence favours Claude.
Can I run Grok 4.7 locally or fine-tune it?
No public evidence supports that. xAI has not released open weights for Grok 4.7 and discloses no self-hosting option, so hardware, VRAM and quantization questions do not apply. You can reach it through the xAI API, Grok Build, Cursor, GitHub Copilot, and several clouds and routers, all as hosted inference. If you need local inference, consider open-weight models and read our comparison of local LLM runtimes instead.
Further Reading
- Grok 4.5 explained: architecture and benchmarks, the predecessor generation in this series.
- Agentic IDEs compared: Cursor, Windsurf and Claude Code, for the harness side of coding-agent choice.
- Agentic AI security and prompt injection, the threat model for any tool-using coding agent.
- Reasoning effort control and thinking budgets, how effort dials trade accuracy for tokens.
- LLM prompt caching architecture and economics, for protecting the cached-input discount.
- External: xAI, Introducing Grok 4.7; Tech Insider, xAI launches Grok 4.7 at $2/$6; Lindy, Grok 4.7 pricing, benchmarks and what changed.
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