XENNS Mangird Tea Review: Ranked #751 of 1253

Last updated: October 16, 2026

The XENNS Mangird Tea ranks #751 out of 1253 total In-Ear Headphones for sound quality. See all Headphones rankings for every top pick.

Brand: Xenns

Prices on this page were last checked October 16, 2026 at 5:46 PM UTC. Product prices and availability are accurate as of that date/time and are subject to change; the price and availability shown on Amazon at the time of purchase will apply to the purchase of this product.

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XENNS Mangird Tea - ranked #751 of 1253 In-Ear Headphones · Editor's rating 3.6/5

#751 XENNS Mangird Tea: $299 Discontinued eBay

Newer model: the Xenns Mangird Tea Pro, ranked higher, rated 4/5, released Aug 2024. See every Xenns Mangird Tea generation.

Pros: A broadly balanced tuning with a preference score of 69 against the Harman target.

Cons: Treble about 5 dB below target from 3 to 10 kHz, which can sound dull or veiled next to brighter headphones. An uneven response, 2.4 dB standard deviation from target. Discontinued.

Is the XENNS Mangird Tea right for you?

Buy it if

  • you are sensitive to bright or sharp treble (treble sits 5.1 dB below target).
  • you are happy to use EQ: our preset lifts its Harman match by about 16 points.

Skip it if

  • you want more bass (about 3 dB below target).
  • you want to buy it new: it is discontinued.
BackClosed
ConnectionWired (3.5mm)
Deviation From Target2.38 dB standard deviation
Driver TypeHybrid
Drivers1 Dynamic, 6 Balanced Armature
Form FactorIn-ear
Harman Preference Score69 (median of 7 squig.link measurements, calibrated to AutoEQ)
Sub-bass ExtensionWithin 6 dB of target down to 20 Hz (-2.8 dB at 30 Hz)
Tonal Tilt-0.24 (negative is warmer, positive brighter)

EQ Presets

Harman target

Set a preamp of -5.3 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Harman match with this EQ: 85 out of 100, up from 69.

FilterFrequencyGainQ
Low shelf105 Hz+3.0 dB0.70
High shelf10,000 Hz-0.3 dB0.70
Peak23 Hz-1.2 dB3.38
Peak164 Hz+1.1 dB0.57
Peak1,044 Hz-1.4 dB2.16
Peak3,100 Hz+1.6 dB2.83
Peak5,345 Hz+5.2 dB0.61
Peak7,006 Hz-3.2 dB5.92

Neutral target

Set a preamp of -5.3 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Neutral match with this EQ: 86 out of 100, up from 45.

FilterFrequencyGainQ
Low shelf105 Hz-5.6 dB0.70
High shelf10,000 Hz-0.3 dB0.70
Peak23 Hz-1.6 dB2.56
Peak191 Hz+1.1 dB0.84
Peak1,045 Hz-1.4 dB2.16
Peak3,101 Hz+1.6 dB2.83
Peak5,345 Hz+5.2 dB0.61
Peak7,006 Hz-3.2 dB5.92

Studio flat target

Set a preamp of -6.4 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Studio flat match with this EQ: 88 out of 100, up from 28.

FilterFrequencyGainQ
Low shelf105 Hz-8.2 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak23 Hz-1.9 dB2.14
Peak906 Hz-1.5 dB0.74
Peak3,148 Hz+6.0 dB0.88
Peak5,504 Hz+3.8 dB4.57

Warm target

Set a preamp of -5.5 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Warm match with this EQ: 91 out of 100, up from 75.

FilterFrequencyGainQ
Low shelf105 Hz+3.2 dB0.70
High shelf10,000 Hz-3.9 dB0.70
Peak164 Hz+1.3 dB0.58
Peak2,916 Hz+1.9 dB3.22
Peak5,345 Hz+5.6 dB1.22
Peak7,531 Hz-2.8 dB5.54

Bass target

Set a preamp of -6.4 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Bass match with this EQ: 86 out of 100, up from 61.

FilterFrequencyGainQ
Low shelf105 Hz+6.0 dB0.70
High shelf10,000 Hz-0.3 dB0.70
Peak103 Hz+1.4 dB0.68
Peak1,024 Hz-1.4 dB1.90
Peak3,101 Hz+1.6 dB2.90
Peak5,345 Hz+5.2 dB0.61
Peak7,006 Hz-3.2 dB5.91

Bright target

Set a preamp of -6.4 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted Bright match with this EQ: 77 out of 100, up from 32.

FilterFrequencyGainQ
Low shelf105 Hz-5.6 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak23 Hz-1.6 dB2.44
Peak194 Hz+1.0 dB0.88
Peak1,098 Hz-1.6 dB1.62
Peak3,573 Hz+6.0 dB0.92
Peak5,517 Hz+3.0 dB4.84

V-shaped target

Set a preamp of -6.5 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 7 squig.link measurements, in the filter layout AutoEQ uses. Predicted V-shaped match with this EQ: 74 out of 100, up from 54.

FilterFrequencyGainQ
Low shelf105 Hz+6.0 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak156 Hz+2.8 dB0.73
Peak371 Hz+0.9 dB2.53
Peak1,076 Hz-2.2 dB1.50
Peak3,354 Hz+6.0 dB0.88
Peak5,455 Hz+3.2 dB4.14

Frequency Response

How the XENNS Mangird Tea measures across the audible range, against the Harman in-ear target that most listeners prefer in blind tests. Where the line sits above the target that range sounds louder (more bass, brighter treble), below it quieter. The curve is the median of 7 independent measurements, so one odd sample or rig cannot skew it, and both lines are shifted so their 500 Hz to 2 kHz average sits at 0 dB.

XENNS Mangird TeaHarman target
Measured frequency response of the XENNS Mangird Tea-24-18-12-60+6+12+18dB20501002005001k2k5k10k20k

Measurement data: median of 7 measurements from the squig.link databases of aden, breampike, nymz, pw, superreview, vsg and yoshiultra. Thanks to each of them for publishing their work.

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