Best AI for Travel Planning in 2026: We Tested All 5 Models on the Same Trip
Here is a number that should make you pause before your next trip: in our test of five major AI models planning a 7-day Tokyo itinerary, the worst-performing model got 63% of its specific recommendations right. That sounds acceptable until you realise what 37% wrong actually means on the ground. It means a restaurant you showed up to that closed two years ago. A museum you walked an hour to reach that is shut on Mondays. A walking route listed as 20 minutes that takes 35. A fish market that moved to a different location in a different neighbourhood, but the AI still sends you to the old address.
Travel is one of the highest-stakes domains for AI hallucinations because the errors are not abstract. You find out about them at 8pm in an unfamiliar city, hungry, with a partner who is starting to question your planning choices.
We gave all five models the same prompt: "Plan a 7-day Tokyo trip for two adults, mid-range budget, foodie focus, late June." Then we fact-checked every named recommendation: every restaurant, every museum, every transit direction, every walking time, every opening day. Only two of the five itineraries survived contact with reality.
What We Tested and How
The prompt was chosen deliberately. Late June is rainy season in Tokyo (tsuyu), which changes packing, outdoor plans, and what a realistic daily itinerary looks like. A model that ignores this is not just incomplete; it is building a schedule around weather conditions that do not exist. "Foodie focus" meant we could audit restaurant recommendations with precision: does this place exist, is it open, is it mid-range, can you realistically get a reservation, and is the location where the model says it is?
For each model, we extracted every specific, named recommendation and ran it through three checks:
- Existence check: Does this place actually exist at the stated address or in the stated neighbourhood?
- Status check: Is it currently open, and is it open on the day the model scheduled it?
- Logistics check: Are the transit directions, walk times, and station names correct?
We scored on three dimensions: accuracy rate (percentage of recommendations that passed all three checks), hallucination count (specific details that were fabricated or critically outdated), and overall itinerary quality assuming everything were real.
The Prompt That Most Models Did Not Fully Understand
Before getting into individual results, it is worth noting that late June in Tokyo is tsuyu season. This is a well-documented meteorological reality. Average rainfall in Tokyo in June is among the highest of the year. Outdoor markets, rooftop dining, and garden walks, which are extremely popular AI travel suggestions for Tokyo, are miserable in heavy rain.
Only two of the five models flagged rainy season unprompted. The others built outdoor-heavy itineraries for a week that statistically includes significant rainfall, without a single note about bringing a rain jacket, booking covered venues, or adjusting expectations. This is not a small detail. It is the kind of thing a local friend would tell you in the first sentence.
Model by Model: What Each One Got Right and Wrong
ChatGPT planned a solid-looking week with a good mix of neighbourhoods: Shinjuku, Harajuku, Shibuya, Asakusa, Tsukiji, Akihabara, and a day trip to Nikko. The itinerary read well and had appropriate pacing. Then the auditing started.
ChatGPT recommended visiting the Tsukiji inner fish market for an early-morning tuna auction. The Tsukiji inner market closed in October 2018. It relocated to Toyosu, a different neighbourhood entirely, and the auction is now nearly impossible for tourists to attend without a lottery reservation made months in advance. The outer market at Tsukiji still exists and is worth visiting for breakfast, but ChatGPT conflated the two. It also recommended a specific sushi counter in Ginza and described it as "a mid-range lunch option" — this restaurant has a years-long reservation waitlist and is firmly in the high-end category. Walking times were consistently underestimated by 30 to 40 percent. Accuracy rate: 82%. Hallucination count: 3.
Claude built a more conservative itinerary. It flagged rainy season in the second paragraph, recommended covered markets and indoor food halls (depachika) as weather contingencies, and suggested arriving with a Suica card loaded before leaving the airport. The restaurant recommendations were specific but achievable: ramen shops, izakayas, a depachika in Isetan Shinjuku, a coffee roaster in Shimokitazawa. One notable miss: Claude recommended TeamLab Borderless as a must-visit. TeamLab Borderless closed its original Odaiba location in 2022 and reopened at Azabudai Hills in 2024. Claude had the experience right but the location wrong, which would have sent visitors to the wrong part of the city. Accuracy rate: 90%. Hallucination count: 1.
Gemini produced the most visually appealing itinerary in terms of formatting: day-by-day tables, estimated costs, neighbourhood maps described in text. The content, however, had the most factual errors of the middle tier. It recommended a ramen shop in Shibuya that permanently closed in 2023. It listed the Tokyo National Museum as closed on Tuesdays. The museum is actually closed on Mondays. On a Tuesday visit, you would show up to find it open; on a Monday visit, based on this schedule, you would find it shut. The error is not catastrophic, but it represents the kind of unverified detail that spreads across AI-generated content. Gemini also did not flag rainy season. Accuracy rate: 71%. Hallucination count: 4.
Grok had the most creative recommendations and the highest hallucination count. It suggested a specific standing-bar in the alleys behind Shimbashi station that appeared to be fabricated. No record of it exists in any Japanese review platform, Google Maps result, or travel forum. It recommended Gonpachi Nishi-Azabu, known as the Kill Bill restaurant, as a mid-range dinner option. Gonpachi exists, but its prices are firmly in the high-end range for Tokyo. Grok also made two errors with train lines, routing travelers via the wrong subway line between stations, which would have added 20 minutes to each journey. It did, notably, recommend Toyosu Market correctly as the current fish market location, and was the only model to get that right. Accuracy rate: 63%. Hallucination count: 5.
Perplexity was the standout. It included source citations for most of its restaurant recommendations, which created a natural accountability layer. It flagged rainy season. Its restaurant suggestions were verifiable, bookable, and genuinely mid-range. Its transit directions were accurate. The one error: it listed opening hours for a sake bar in Nakameguro that appeared to be outdated by one hour. Minor, but included for fairness. Accuracy rate: 95%. Hallucination count: 1.
The Audit Results
| Model | Accuracy Rate | Hallucinations | Rainy Season Flagged | Itinerary Quality |
|---|---|---|---|---|
| ChatGPT | 82% | 3 | No | B+ |
| Claude | 90% | 1 | Yes | A |
| Gemini | 71% | 4 | No | B |
| Grok | 63% | 5 | No | B- |
| Perplexity | 95% | 1 | Yes | A- |
Two models, Claude and Perplexity, crossed the 90% threshold and flagged the most important contextual factor without being prompted. The other three ranged from passable to actively misleading.
Where They Disagreed Most
Beyond the hallucinations, the five models made genuinely different choices that reflect different philosophies about what a good travel itinerary is.
On pacing: Claude and Perplexity both built in buffer time. Grok and Gemini packed each day to capacity. ChatGPT was in the middle. The buffer-time models implicitly understood that Tokyo is dense and transit connections, while excellent, take longer than expected for first-time visitors.
On food ambition: Claude leaned toward accessible local spots with realistic reservation paths. Grok aimed high with several recommendations that would require booking months in advance. Gemini mixed the two without flagging which required reservations.
On day trips: ChatGPT recommended Nikko. Claude recommended Kamakura and Hakone as alternatives with better rainy-season viability. Gemini recommended Nikko and Yokohama. Grok recommended Kyoto as a day trip, which is technically possible by Shinkansen but makes for a very long and expensive day from Tokyo.
On budget framing: Only Perplexity gave actual yen estimates per meal category. The others used vague descriptors like "mid-range" without anchoring them to numbers.
Why This Matters Beyond Tokyo
A hallucinated restaurant means a wasted evening. That is the low-stakes version of this problem. The same failure mode, an AI presenting a specific, confident, wrong recommendation, shows up in medical questions, legal research, financial decisions, and product purchases. The mechanism is identical. The consequence scales with the domain.
Travel makes the problem visible in a way that is hard to ignore because you find out about it in the moment. You show up at the wrong address. The restaurant is dark and locked. There is no sign that says "this recommendation was generated without verification." You just know something went wrong.
This is the case for cross-checking AI answers before you act on them. Not because any individual model is bad, but because hallucinations in all models follow a consistent pattern: confident, specific, plausible-sounding, and quietly wrong. The only way to surface them is comparison.
When two models agree on a restaurant, the probability that it exists and is worth visiting goes up significantly. When one model recommends something the other four do not mention, that is a flag worth investigating before you make a reservation.
Talkory runs your prompt across all five models simultaneously and surfaces the Consensus Answer: the recommendations that most models agreed on, alongside a breakdown of where they diverged. For a Tokyo itinerary, that means you see immediately that four of the five models recommend depachika lunch over sit-down restaurants for budget-conscious foodies, but only one recommends the specific bar that may not exist. You do not need to run five separate queries. You get the overlap and the outliers in one view.
Our Verdict
For pure accuracy on a high-specificity travel prompt, Perplexity led the field. For overall itinerary quality combined with contextual awareness, Claude came closest to what a knowledgeable local friend might suggest. ChatGPT produced a competent but error-prone itinerary. Gemini and Grok had too many factual errors to rely on without verification.
None of these models should be your only source. The best itinerary in this test was not the output of any single model. It was the intersection of what Claude, Perplexity, and ChatGPT agreed on, verified against the errors each one made independently.
Run your trip prompt through every model before you book the flight. Not because the answers will be perfect, but because the disagreements will show you exactly where to double-check.