MangoFinch vs Google Meet captions vs Microsoft Teams transcription

I run multilingual test meetings every week. Same script, same speakers, same language switches. One person reads a paragraph in English, switches to Japanese for a technical term, another speaker responds in Portuguese, and a third summarizes in French.

Then I compare what Google Meet captured, what Microsoft Teams captured, and what MangoFinch captured. The results are consistent enough at this point that I can write this comparison without hedging.

This is not a marketing page. I will tell you where the built-in tools are better than us.

Google Meet auto-captions

Google Meet offers real-time captions in the meeting view. As of early 2026, here is what it does:

**Language support:** Meet supports captions in 4 languages — English, French, German, and Spanish. Google expanded from English-only in late 2024 and added French, German, and Spanish in 2025. Portuguese, Japanese, Mandarin, and Korean are not supported for captions.

**Translation:** Meet added translated captions in late 2025 for Google Workspace Business Standard and above. Translations work from the 4 supported caption languages into 16 target languages. Each participant picks their preferred language and sees translations inline. This is a meaningful feature, though limited by the small number of source languages.

**Accuracy:** For English in a quiet environment with a native speaker on a good microphone, Meet's captions are about 92-94% accurate by our testing. We measured this by comparing caption output against a human-verified transcript, word by word, across 40 test segments. That accuracy drops to 85-88% with non-native English speakers, background noise, or fast speech.

**Code-switching:** Meet does not handle code-switching. If you set captions to English and someone speaks Spanish, the Spanish words get transliterated into English phonetic approximations. "Necesitamos revisar" becomes "nesse see Thomas rebisar" or similar nonsense. There is no mid-stream language detection.

**Transcript storage:** Meet saves transcripts to Google Drive automatically for Workspace users. The transcripts are searchable, shareable, and linked to the calendar event. This is excellent organizational integration that neither Teams nor MangoFinch matches in convenience.

**Cost:** Included with Google Workspace. No additional per-minute charge for captions.

Microsoft Teams transcription

Teams has been building out transcription and captioning aggressively, especially with Copilot integration.

**Language support:** Teams supports live captions in 34 languages as of early 2026. This is the most extensive language list of the three options. Teams also supports meeting transcription (stored text records) in 16 languages.

**Translation:** Teams allows each participant to see captions translated into their preferred language from the spoken language. This works across the supported caption languages. The translation happens on Microsoft's side, using Azure Cognitive Services.

**Accuracy:** Our testing shows Teams at 90-93% accuracy for English, which is comparable to Meet. For other languages, accuracy varies more widely. German transcription was solid at 89%. Japanese was noticeably weaker at 82%. Mandarin was around 84%. These numbers are from our controlled tests — real-world accuracy will vary with audio quality, accents, and vocabulary.

**Code-switching:** Teams introduced a multilingual transcription preview in late 2025 that attempts to handle meetings with multiple spoken languages. In our testing, it handles clean language switches — one speaker finishes in English, the next speaks in Spanish — reasonably well. It struggles with mid-sentence switches. When a German speaker drops an English technical term into a German sentence, the English term often gets transcribed as if it were German. The detection latency after a switch is 3-5 seconds, during which the transcript is often garbled.

**Copilot integration:** This is Teams' strongest differentiator. Copilot can summarize the meeting, extract action items, and answer questions about the discussion after the fact. It works on top of the transcript. For single-language meetings, this is genuinely useful. For multilingual meetings, the quality of Copilot's output depends on the quality of the underlying transcript, which means it degrades when code-switching is present.

**Transcript storage:** Saved in the Teams meeting record, accessible to participants. Integrates with Microsoft 365 ecosystem. Less convenient than Meet's Drive integration for non-Microsoft shops, more convenient for Microsoft shops.

**Cost:** Live captions are included in most Teams plans. Meeting transcription requires Teams Premium ($7/user/month) or Microsoft 365 Copilot ($30/user/month). Copilot features require the Copilot license.

MangoFinch

Now our own product, with the same honesty.

**Language support:** 36 languages for transcription. All 36 can be active simultaneously in a single meeting. There is no language selection step — the engine detects what is being spoken per segment.

**Translation:** Every transcribed segment is translated into each participant's preferred language via our translation engine. Translations appear inline beneath the original text. All 36 languages can be both source and target.

**Accuracy:** Our overall accuracy across all languages in general vocabulary is consistently among the best available. English specifically tests at about 95-96% accuracy, which is slightly better than Meet and Teams. Japanese, Mandarin, and Portuguese all perform in the low 90s. These numbers are from our production monitoring across thousands of transcribed segments.

**Code-switching:** This is where we are meaningfully different. MangoFinch handles mid-sentence language switches with about 94% detection accuracy for segments over 3 seconds. The engine evaluates each audio segment independently rather than committing to one language for the meeting. When someone says "We need to finalize the Vertrag before Friday," the transcript shows "We need to finalize the" in English and "Vertrag" in German, with appropriate translations.

**No native platform integration:** This is our biggest weakness. MangoFinch is not built into Meet or Teams. It runs as a separate application. You open MangoFinch in a browser tab alongside your video call and route audio to it. There is no one-click "enable MangoFinch" button inside Meet or Teams. We are working on integrations, but they are not here yet.

**Transcript storage:** Transcripts are stored in MangoFinch and accessible via the web app. We do not currently integrate with Google Drive, Microsoft 365, or Notion. Export is available as JSON, plain text, or SRT subtitle format.

**Cost:** Free tier includes 5 hours per month. Pro plan is $12/month for 40 hours. Team plan is $8/user/month for unlimited hours with admin controls.

The comparison matrix

Here is the feature matrix, stripped of marketing language:

| Feature | Google Meet | Microsoft Teams | MangoFinch |

|---------|------------|-----------------|------------|

| Caption languages | 4 | 34 | 36 |

| Translation | 4 source to 16 target | Multi-language pairs | 36 to 36 |

| Code-switching | No | Preview (limited) | Yes (94% detection) |

| English accuracy | 92-94% | 90-93% | 95-96% |

| Non-English accuracy | N/A (limited languages) | 82-89% varies | 90-93% varies |

| Native integration | Built into Meet | Built into Teams | Separate app |

| AI meeting summary | Gemini (Workspace) | Copilot ($30/user/mo) | Not yet |

| Transcript search | Google Drive | Teams/M365 | MangoFinch app |

| Additional cost | Included | $0-30/user/month | $0-12/month |

When to use built-in captions

Use Google Meet or Teams built-in captions when:

**Your meetings are single-language.** If everyone speaks English (or any one supported language), the built-in tools are accurate enough and the cost is zero. There is no reason to add another tool.

**You need platform integration.** If your workflow depends on Google Drive or Microsoft 365, the native transcript storage is more convenient than exporting from a separate app.

**You want AI summaries.** Copilot and Gemini can summarize meetings, extract tasks, and answer questions about what was discussed. MangoFinch does not do this yet.

**Budget is the primary constraint.** Built-in captions are free with your existing platform subscription. For a 50-person company, even $8/user/month adds $4,800/year.

When to use MangoFinch

Add MangoFinch when:

**Your team speaks multiple languages in the same meeting.** This is the core use case. If three or more languages appear regularly in your meetings, built-in tools will produce broken transcripts for the non-primary language segments.

**You need code-switching support.** If speakers switch languages mid-sentence — common in technical discussions, international teams, and bilingual workplaces — MangoFinch is the only option that handles it reliably.

**You need cross-language search.** MangoFinch indexes transcripts with both original language and translations. Search for a concept in any language and find it regardless of what language it was spoken in.

**Translation accuracy matters more than convenience.** Because we translate every segment individually with detected source language, the translation quality is higher than translating from a potentially garbled single-language transcript.

A specific example

Last month I ran a test meeting with 4 speakers: English, Japanese, Brazilian Portuguese, and German. The meeting was 22 minutes long with 47 language switches.

**Google Meet (set to English):** Captured English segments well (93% accuracy). Japanese segments became English-phonetic gibberish. Portuguese segments were partially captured because some Portuguese words happen to sound like English words. German segments had similar issues. Overall usable accuracy: about 41% of the total meeting content.

**Microsoft Teams (multilingual preview):** Captured English at 91% accuracy. Detected Japanese switches after 3-4 seconds, then transcribed Japanese segments at about 80% accuracy. Portuguese detection was slower, about 5-6 seconds, with 78% accuracy. German was strongest at 86% after detection. Mid-sentence switches were mostly missed. Overall usable accuracy: about 72% of the total meeting content.

**MangoFinch:** Captured English at 96% accuracy. Japanese at 90%. Portuguese at 92%. German at 93%. Language detection happened within 1.2 seconds on average. Mid-sentence switches were caught 89% of the time. Overall usable accuracy: about 92% of the total meeting content.

The 92% vs 72% vs 41% gap is why MangoFinch exists. For single-language English meetings, that gap disappears and the built-in tools are the right choice.

What we are building next

We know our weaknesses. No platform integration, no AI summaries, no native Google Drive or Microsoft 365 storage.

Our roadmap for 2026 includes a Google Meet add-on (in development now), a Teams bot integration (planned for Q3), and meeting summary features using the multilingual transcript as input. We are also building a Notion integration for teams that use Notion as their knowledge base.

We are not trying to replace Meet or Teams. We are building the multilingual layer that they have not built yet. The best outcome for most teams is using MangoFinch alongside their existing platform — the platform handles video, screen sharing, and calendar integration while MangoFinch handles the multilingual transcription.

How we measured accuracy

I want to be transparent about methodology because accuracy numbers are easy to cherry-pick.

For each platform, we used the same test protocol. Four speakers read from a prepared script containing 2,400 words across 4 languages (English, Japanese, Brazilian Portuguese, German). The script includes 47 language switches, both clean (between speakers) and mid-sentence. Speakers use consumer-grade headset microphones in a quiet room. Each test is run three times and we report the median.

Accuracy is measured word-for-word against the known script. We count insertions (words added that were not spoken), deletions (words missed), and substitutions (wrong words). The word error rate is (insertions + deletions + substitutions) / total reference words. Accuracy is 1 minus the word error rate.

For Google Meet, we captured captions using a screen recording and extracted text with OCR, since Meet does not export caption text in real time. This adds a small margin of error from the OCR step, which we estimate at less than 0.5%.

For Teams, we used the built-in transcript export feature and compared against the script directly.

For MangoFinch, we used the transcript export API and compared against the script directly.

All three were tested on the same audio. Same room, same speakers, same script, same day. We have repeated this test monthly since January 2026. The numbers I cited earlier are from the March 2026 run.

A note on translation quality

Accuracy and translation quality are different measurements. A transcript can be 95% accurate in capturing what was said but produce poor translations if the source text has errors.

This matters more than you might expect. If Google Meet's English-only transcript captures a Portuguese phrase as "nesse see Thomas rebisar," and then Meet's translation feature tries to translate that English gibberish into French, the French output is also gibberish. The translation cannot be better than the transcript it is based on.

MangoFinch has an advantage here because we identify the source language before translating. A Portuguese phrase gets tagged as Portuguese and sent to the translation engine as Portuguese text. The translation is working from correct source material. This is why our translation quality tends to be higher even when raw transcription accuracy is similar — the language detection step prevents the garbage-in-garbage-out problem.

We have not done formal translation quality scoring (BLEU scores or human evaluation) yet. That is on the list for Q3 2026. For now, our claim is architectural: translating from correctly identified source language produces better translations than translating from a monolingual transcript that mangled the non-primary language segments.

If your meetings are in one language, you do not need us. If your meetings regularly cross language boundaries, try the free tier at mangofinch.com and run your own comparison.