Fireplexity vs Perplexity:
what each one gives you.

What each one is

Fireplexity is an open-source AI answer engine from Firecrawl, now at version 2. It is a Next.js and TypeScript app under the MIT licence, with about 2,000 GitHub stars. When you ask a question, it calls Firecrawl's /search endpoint for live web, news and image results. The same call converts each result page to markdown. Fireplexity then streams an answer with inline citations that link to the pages it used. It uses Groq to run the model, shows charts for stock tickers, and deploys to Vercel in about five minutes. You need two API keys: FIRECRAWL_API_KEY and GROQ_API_KEY.

Firecrawl stated its goal clearly: to show that a developer can build an answer engine for a specific field without paying for one. Its announcement told developers to “stop waiting for someone else to build the tool you need.”

Perplexity is a company. It runs its own crawler, search index and model family (Sonar), and it also pays for access to the leading models from other companies. Pro subscribers can use current GPT, Claude and Gemini models, and the top Opus model is limited to the Max plan. Perplexity also offers many features that Fireplexity does not try to match: Deep Research, Labs, Perplexity Computer, image and video generation, file uploads, apps on every platform, and Comet, a browser that has been free since October 2025. The plans are Free, Pro at $20 a month ($16.67 billed annually), Max at $200 a month or $2,000 a year, Enterprise Pro from $40 per seat a month, and Enterprise Max at $325 per seat a month.

Head to head

Cost per query, with the calculation shown

People often say open source is cheaper, but rarely show the numbers. Here is the calculation for one Fireplexity query at list prices.

We assume a typical query: one search that returns up to six results per source type, a scrape of each web result (and possibly each news result), and an 800-token answer from gpt-oss-120b on Groq. Fireplexity cuts each scraped page to about 2,000 characters before sending it to the model, so the input is only about 4,000 tokens, including the prompt.

A note on the model: the repository still uses Kimi K2, which Groq has retired. Groq recommends gpt-oss-120b as the replacement. Switching takes a two-line change in route.ts.

This means a heavy individual user may not save money by self-hosting. A product that handles thousands of queries a day is different, because Perplexity's consumer plans are priced per seat and Fireplexity is priced per query.

For anyone building software, the fair comparison is Perplexity's Sonar API, which is also priced per use. Sonar Pro costs $3 per million input tokens and $15 per million output tokens. There is also a separate fee of $6 to $14 per thousand requests, depending on the search context size you choose. With Sonar, you do not pay input tokens for the retrieved pages, because the request fee covers them. A thousand requests with a short prompt and an 800-token answer cost about $0.60 for input, $12 for output and $6–14 in request fees. That is about 1.9¢ to 2.7¢ per query. Base Sonar, at $1 per million tokens for both input and output, is cheaper. Sonar Reasoning Pro costs $2 per million input tokens and $8 per million output tokens.

At about 1.1–1.7¢ per query, Fireplexity is cheaper than Sonar Pro, but not by much. Almost all of the cost is Firecrawl. The model costs about a tenth of a cent, because the pages are shortened before the model reads them. The price therefore depends on how many pages you scrape. If you scrape a dozen more pages per query, Fireplexity costs more than Sonar. Cost is not a good reason to self-host. Control is.

Citations: both are less reliable than they look

Both systems place numbered citations next to their sentences, which makes the answers look verified. They are not fully verified. The problem comes from how these systems are designed, not from a fault specific to Perplexity.

Independent reviews have found sourcing problems across many answers. In October 2025, the BBC and the European Broadcasting Union reviewed more than 3,000 news answers from Perplexity, ChatGPT, Copilot and Gemini. Of these, 45% had at least one significant issue, 31% had sourcing problems and 20% had major accuracy issues. Reviews of Perplexity found citations that link to homepages instead of articles, to copies and secondary blogs instead of the original publisher, and sometimes to pages that do not contain the quoted sentence. A 2026 paper, Cited but Not Verified, describes the cause: retrieval-augmented generation attaches a source to a claim without checking that the source supports it.

Fireplexity uses the same design, so it has the same problem. The difference is what you can do about it. The prompt, the scoring step that chooses which scraped pages the model sees, and the code that maps citations are all in a repository you can read. If your field needs exact-quote checks, or a rule that prefers the original article over copies, you can add it. With Perplexity, you can only report a bad answer. To check answers from either engine yourself, see our 60-second routine for spotting a bad citation.

This is the most important difference between the two, and it is not about cost or features: only Fireplexity lets you fix the problem.

The scraping lawsuits

Perplexity faces many lawsuits. On 28 May 2026, CNN sued over more than 17,000 scraped stories, videos and images. It is the latest of several publisher lawsuits filed in less than two years. Others came from The New York Times, Dow Jones and the New York Post, Encyclopedia Britannica and Merriam-Webster, the Chicago Tribune and several Japanese publishers. Their main argument is that AI summaries replace visits to the publisher's site instead of sending readers there. Publishers have not been persuaded by Perplexity's trust-and-transparency campaign or its revenue-share program for publishers.

This is not an argument for self-hosting. Fireplexity also works by scraping live pages. If you run it yourself, the legal risk does not go away. It moves from a company with a legal department to you. If you run an answer engine over content you do not own, at any scale, you are responsible for following robots rules, terms of service and attribution requirements. You avoid this risk if you limit Fireplexity to your own documentation, your own catalogue or licensed sources. That is also the use where Fireplexity fits best.

What Fireplexity does not give you

Fireplexity is a demonstration project, not a finished product. It is missing several things a product needs:

• No accounts, login or rate limiting. If you make it public without changes, strangers can run queries on your API keys.
• No evaluation tools. The only way to tell whether a prompt change improved the answers is to read them.
• No Deep Research, Labs or browser automation. It answers a question from a few pages. It cannot run a 20-minute, multi-step research task or control a browser.
• No apps. There is no iOS, Android or desktop app, no browser integration like Comet, and no shareable conversation history, unless you build them.
• No leading model by default. The default Groq model is fast and very cheap, but it is not one of the most capable models. You can switch to one, but your cost per query will rise about tenfold.
• You run everything. That includes uptime, key rotation, cost spikes, abuse and dependency upgrades for a Next.js app.

Perplexity's real advantage is not the answer box. Many tools can now provide that, which is the point Fireplexity was built to show. Its advantage is everything around the answer box: its search index, its apps on every platform, its deals with model providers, and a browser that people keep open all day.

If Fireplexity is not the right Perplexity alternative

Fireplexity is one of several open-source Perplexity alternatives, and it is not the most popular. The main difference between them, and the one that should decide which you run, is how they get information from the web.

For most people, the real choice is Vane or Fireplexity, and it comes down to design. Vane was called Perplexica until March 2026. It has the same maintainer and code, and about 37,000 GitHub stars. It includes a private SearXNG instance, so in its default modes it answers from the short snippets that search engines return. Its Quality mode also scrapes a few selected pages. With a local model through Ollama, everything runs on your own hardware with no cost per query. Fireplexity scrapes the full pages on every query instead. This gives the model complete article text, including pages built with JavaScript, but each query has a cost.

In short: if you want a free, self-hosted Perplexity alternative to use, run Vane. If you need full, current page text, or a small codebase to build into your own product, start with Fireplexity. The star counts, about 37,000 against 2,000, show that the first need is far more common than the second.

If you don't want to host anything, these hosted alternatives suit different needs:

• ChatGPT with search is the closest mainstream competitor. It reasons better, but cites sources less consistently.
• You.com is the most similar to Perplexity in approach, and now offers multi-step research agents.
• Kagi is a paid, privacy-focused search engine. Its AI features are optional, not the main product.
• Exa is a search API for developers.
• Gemini is strongest on recent information and Google integration, and Claude on careful reasoning.

Which one to choose

If you are a developer choosing a search backend rather than a subscription, compare Fireplexity with the Sonar API. Fireplexity is more transparent, gives you control over sources, and costs slightly less. Sonar is faster, needs no maintenance, and uses an existing index, so it does not scrape pages on every query. Both are good choices. Only Fireplexity lets you see and change how it works.

Common questions