Grok 4.7 and GPT-6.1 Sol on one weekly job: scanning recent news about a competitor and sourcing each claim. It covers freshness, citation support, cost and prompts, and ends with a fair way to test both on your own past scans.
Oct 20, 2026 · 10 min read
Pick Sol first when the scan has to find current stories, attach a source to each claim and keep checking costs down. Grok 4.7 is the better starting point when the scan ends in a finished report.
That split comes from a neutral search test1, a research-report benchmark2 and a small study of how well citations back their claims8. The two benchmarks disagree, which is why neither one settles the choice alone.
In a staged workflow, use GPT-6.1 Sol to find stories, merge duplicates and build the claim-and-source list. Use Grok 4.7 as an optional editing pass once the approved claims and their citations are locked. That order is a judgment call and no test has measured it. Both models are recent, since Grok 4.7 launched on September 21, 20263 and GPT-6.1 Sol on September 29, 202615.
You run a recurring scan and need each claim tied to a source that supports it. GPT-6.1 Sol scored higher in the neutral search test and has the stronger public evidence for claim-to-source fit.
You turn the scan into a readable brief for leaders. Grok 4.7 leads one research-report benchmark and charges less for output, so test it first when someone checks the citations.
You resend a long company profile and source history every week. GPT-6.1 Sol has the larger context window and the cheaper cached input.
A wrong claim about a competitor gets costly once it is shared. Let one model build the claim ledger and the other write or challenge the report, then check every citation before it goes out.
This page compares the two models through their API in one neutral setup, with the same search provider, page reader, prompt, time window and source set.
The parts that matter for a news scan are finding current stories, attaching a source to each claim, checking that the source supports the claim, writing the findings up, and cost. We used official docs first, then independent benchmarks and one small citation study.
We left tools out of the spec table on purpose. Each vendor sells its own built-in search, and those are retrieval systems around the model that behave differently in a chat product, in the API and inside a workspace. A win for one of them would be a win for the wrapper, so none of them decides a verdict here.
The model facts that affect a scan job. Prices and limits were checked on October 20, 2026, and built-in search tools are left out.
Figures from xAI and OpenAI documentation, checked October 20, 2026. Cross-vendor cost comparisons are directional, since the two companies count tokens and price long prompts differently.
The answer changes by subtask. Each row below names the model with the edge on one part of a scan and the evidence behind it.
Better-choice calls map to dimensions the sources evaluated. Where the evidence is indirect or missing for one model, the row says so.
A useful test is boring. Same tools, same cutoff time, same prompt, same scoring, then judge what an analyst pays for: right period, real sources, full support and little editing.
Choose weeks you have already scored: one quiet week, one with many announcements and one with conflicting reports. You know the important stories and the false alarms, so every claim can be checked against a known answer.
Use one prompt with the same competitor profile, event types, cutoff time and source-domain rules. Require a claim ledger and a report format. Neither model gets a richer version.
Give both the same search and page-reading tools, and the same reasoning level where possible. API and chat-product results can differ because tools, system prompts and effort settings differ, so test where the team will work.
Do not edit before scoring. For each claim check the period, whether an old story was passed off as new, whether the cited page supports the whole wording and whether hedges and denials survived. Report citation coverage and citation support separately, and use blind human review for commercial work.
No public test measures both exact models on recent competitor news, so the best evidence is a mix. Here is what each source helps judge.
LiveResearchBench suggests judging research on freshness, coverage, presentation and whether each claim matches its citation, and not on citation count alone12.
Both models do better when the prompt asks for a source on each claim, not a source list at the end.
For Grok 4.7, use its structured output and make the source check explicit. xAI's full list of encountered URLs can include pages the answer never used, so ask for claim-level inline links or source IDs14, 10.
For GPT-6.1 Sol, give each source a stable ID and ask for citations to the smallest block that supports each sentence. OpenAI's guidance recommends clearly marked citable units, explicit citation syntax and a rule for what to do when support is missing13.
A Grok 4.7 prompt: structured output with an excerpt per claim
Scan public information published from October 13 through October 19, 2026.
Return JSON with one object per material event:
claim, event_date, publication_date, source_id,
exact_supporting_excerpt, support_status and confidence.
Prefer primary announcements.
Exclude claims unless the cited excerpt supports the complete wording.
Label social posts and unconfirmed reports separately.A GPT-6.1 Sol prompt: block-level citations and an audit table
Research competitor developments published from October 13 through October 19, 2026.
For every factual sentence, cite the smallest retrieved block that supports it.
Preserve uncertainty and hedging.
If no block supports the full sentence, split the sentence,
weaken it, or mark it unsupported.
End with a claim-source audit table and a list of search gaps.Citation support has to be checked outside the model. The useful question is where each one adds cleanup work and what to change in the prompt or the workflow.
One question first: is the scan mainly an evidence ledger or a finished research report? Then follow the branch that matches most of your weeks.
A starting point and not a rule. Test on your own past scans first.
If the scan is an evidence ledger, or you want the least analyst checking, start with GPT-6.1 Sol. The search result1 and the citation study8 both lean its way. If the scan ends as a finished report that a person checks line by line, test Grok 4.7 first because of its LiveBench Research result2.
When a single request needs more than 500,000 tokens of source history, choose GPT-6.1 Sol, since Grok 4.7's window is smaller4, 6. Reports with long output and a small, uncached input may cost less on Grok 4.7. A weekly scan that reuses a large stable dossier costs less on GPT-6.1 Sol because of its cached-input price4, 6.
For high-stakes legal, financial or reputational claims, neither model should publish on its own. If you must pick a starting model, choose GPT-6.1 Sol, then add automatic citation checks and human approval. If the choice is still unclear, run both on five past scans and keep the one with fewer material unsupported claims, regardless of how polished its prose looks.
For a sourced weekly competitor scan, start with the larger model. It has the better exact-version search result, the larger context, cheaper caching and the stronger public evidence that citations match claims. Grok 4.7 fits when report writing and lower output pricing matter more and a separate step verifies citations.
The limits are real. The main search and research benchmarks disagree, provider setups vary, and no public test measures both models' rate of unsupported citations on recent competitor news. Prices and behavior also change fast, so treat this as a starting hypothesis.
The safest final step is to test the shape of your own work, not a generic prompt from the internet. A fair test needs the same setup for both models: the same competitors, the same sources, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first scan comes back. The cleaner the setup, the more the difference you see is really Grok 4.7 vs GPT-6.1 Sol, and not just which one happened to be easier to reach that day.
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