Meeting transcription tools all promise the same thing โ accurate, searchable records of what was said โ but they get there in very different ways, and those differences matter more than any headline accuracy number. Some are consumer-friendly bots that join your calendar automatically. Some are built for revenue teams and plug straight into a CRM. One isn't a meeting app at all, but the raw speech-to-text engine that powers products like it. This piece focuses specifically on the transcription and capture layer: how these tools get audio into accurate, usable text, not the broader note-taking or workspace features layered on top.
Why transcription quality is hard to compare on paper
Every vendor in this space will show you an accuracy number, and you should treat all of them skeptically. Accuracy depends heavily on audio quality, accents, cross-talk, industry jargon, and number of speakers โ a demo recorded in a quiet studio with two clear speakers tells you very little about how a tool handles a seven-person call with someone dialing in from a car on a bad connection. The more useful signal is how a tool handles the failure cases: does it flag low-confidence sections, does speaker attribution hold up when two people talk over each other, and can you quickly correct errors without editing a wall of unstructured text. Judged on those terms, the differences between tools become much clearer than raw accuracy claims suggest.
It also helps to separate three things that get bundled under "transcription" but are really separate engineering problems: converting speech to text, attributing each line to the right speaker, and structuring that raw text into something skimmable โ summaries, action items, timestamps you can jump to. A tool can be excellent at one and mediocre at another, and most of the meaningful differences between these products show up in the second and third categories rather than in raw word-level accuracy, which has converged quite a bit across vendors in recent years.
Otter.ai and Fireflies.ai: the two default choices
Otter.ai remains the most familiar name in this category, and for live transcription specifically, it's hard to beat โ you can watch words appear on screen in real time during the call, which is genuinely useful for anyone who wants to follow along or highlight key moments as they happen rather than reconstructing them afterward. Speaker identification improves the more it hears a given voice, which matters for recurring meetings with the same participants but is less reliable on one-off calls with strangers, where it tends to fall back on generic labels like "Speaker 1" until someone corrects them. Its free tier includes a real monthly allotment of transcription minutes, which makes it one of the easier tools to just try without a sales conversation, and the mobile app's ability to transcribe in-person conversations (not just video calls) is a feature its meeting-bot competitors mostly don't bother with.
Fireflies.ai matches Otter on core transcription quality and differentiates on what happens after the transcript is generated โ automatic topic detection, sentiment tagging, and the ability to query across your entire meeting history conversationally rather than searching one transcript at a time. Where Fireflies pulls ahead for revenue teams specifically is CRM integration: transcripts, call notes, and extracted action items can push directly into Salesforce or HubSpot, turning a call recording into a logged CRM activity without manual entry. Its library of pre-built summary templates for specific meeting types โ sales calls, standups, interviews โ also means less manual tidying of AI output than tools that generate one generic summary format for everything. If you're deciding between these two, the head-to-head Fireflies vs. Otter comparison walks through pricing tiers and integration depth in more detail than is useful to repeat here.
Fathom: speed over depth
Fathom takes a deliberately narrower approach โ fast, accurate transcription and an editable summary within moments of the call ending, wrapped in one of the more generous free plans in the category. It supports Zoom natively and has extended to Teams and Meet, and its clip-and-share feature (pulling a specific moment out of a call to send to someone who wasn't there) is genuinely useful for sales and customer success teams who need to share proof points without sending a full recording or making a colleague scrub through forty minutes of audio. What it doesn't try to be is a deep analytics or CRM platform โ those features exist but sit behind paid tiers and are less built-out than Fireflies' equivalent. Where Fathom tends to win is onboarding friction: there's very little setup between installing it and getting a usable summary from your first call, which matters if you're trying to get an entire team to actually adopt a transcription tool rather than abandon it after the trial meeting.
Avoma: transcription as part of revenue intelligence
Avoma is worth separating out because it isn't really competing on transcription as a standalone feature โ it's a conversation intelligence platform for sales and customer success teams, and transcription is the input layer for deal scorecards, coaching workflows, and pipeline insights pulled from call content. If you just want accurate meeting notes, Avoma is more tool than you need, and the learning curve reflects that: it has more configuration surface area than a simple note-taker, with scorecards, talk-ratio tracking, and topic trackers that need to be set up before they're useful. If you're a revenue team that wants to track talk-ratio, identify competitor mentions across calls, or coach reps based on what they actually say on calls, Avoma's transcription quality is a means to a much bigger end than the other tools here attempt, and it also bundles in scheduling links, which the pure note-taking tools don't touch at all.
AssemblyAI: the engine, not the app
AssemblyAI belongs in this conversation for a different reason: it's a speech-to-text API, not a meeting assistant you'd install and use directly. Developers use it to build transcription into their own products โ customer support tools, call-center analytics, custom meeting apps โ rather than as an end-user bot that joins your Zoom calls. If you're evaluating meeting-note apps for your team, AssemblyAI isn't a competitor to Otter or Fireflies in the way you'd experience it day to day; there's no calendar integration, no summary UI, no dashboard of past meetings. But it's genuinely relevant if you're building something in-house and want to know what transcription quality is achievable at the API level, since several consumer-facing tools in this space are built on general-purpose speech recognition engines in roughly the same category. Its documentation and model options (different tiers trading off speed against accuracy, plus features like speaker diarization and PII redaction exposed directly as API parameters) make it a common default for teams that have decided buying a finished meeting app isn't the right fit for their product.
Krisp: transcription bundled with noise suppression
Krisp started as a noise-cancellation tool โ stripping background noise, keyboard clatter, and echo from calls in real time โ and has since added meeting transcription and note generation on top of that core product. That history matters: Krisp's transcription accuracy benefits directly from the fact that it's already cleaning the audio signal before transcription happens, which can be a real advantage on calls with noisy environments, unstable connections, or participants without good microphones. Its noise suppression also runs locally on-device for the audio-processing layer, which is part of why it's often positioned as the more privacy-conscious option of the group. It's a reasonable pick if noise suppression is a primary need and transcription is a nice bonus, but teams whose only requirement is meeting notes will likely find purpose-built tools like Otter or Fireflies more full-featured on the notes side โ Krisp's summaries and search are functional rather than a core strength.
Privacy and where audio actually goes
This is the question most comparisons skip, and it shouldn't be an afterthought. Every bot-based transcription tool needs to either join your call as a visible participant or capture system audio, and every vendor has a different policy on how long raw audio is retained, whether it's used to train models, and whether external meeting participants are notified that a recording bot has joined. Before rolling any of these out across a team, it's worth reading the actual data retention policy rather than assuming defaults are conservative โ this varies more between vendors than transcription accuracy does, and it's the detail most likely to cause a problem after the fact, particularly for calls involving clients, candidates, or anyone outside your own organization. It's also worth checking whether a tool lets you delete recordings and transcripts on a schedule, since "we keep everything forever by default" is a more common setting than people expect, and cleaning it up after the fact is much harder than configuring retention correctly on day one.
Picking the right one
For general-purpose meeting transcription with the best live-transcript experience, Otter is still the safest default. For teams that want notes plus CRM logging, Fireflies is the stronger pick. For speed and a genuinely usable free tier with minimal setup, Fathom. For sales teams that want coaching and deal intelligence built on top of transcripts rather than just a record of them, Avoma. For noisy environments where audio quality is the bigger problem than note-taking, Krisp. And if you're building your own product rather than buying one, AssemblyAI is the place to start.
None of these choices are permanent โ most teams end up trying two or three before settling, since the free tiers make that cheap to do, and the real differences only show up once you've run a messy, real meeting through each one rather than a clean demo call. Once you've settled on a capture tool, the next question is usually where those transcripts and summaries actually live day to day โ our companion article on AI note-taking tools compared covers the workspace side of that question, including how these transcription tools fit alongside general AI writing assistants that handle drafting and organization rather than call capture.
