As a commercially important tuna in the high seas, bigeye tuna () has become one of the most important target species in Chinese distant-water fisheries. The research and management of is receiving more interest, especially for its life history, stock assessment, and fishery management. The fish life history traits including growth, is the fundamental and key process of population dynamics and stock assessment, with an increasing attention in recent years. Based on the data collected by Chinese observers onboard from 2013 to 2018, the growth of the bigeye tuna was analyzed, with the spatial-temporal variations. A total of 8806 individuals were measured, including the wet weight (kg; after removing gills, gut, and tail) and fork length (cm). The predicted power length-weight function indicated that the estimated condition factor , while the estimated allometric growth parameter is 3.08 (3.07-3.10). Mixed effects models were established to estimate the variations from different years, quarters, and regions, while 7 model candidates were considered with different random effect sources. AIC (Akaike Information Criterion) and Root Mean Square Error values revealed that the mixed effect model performed best with all variations from years, quarters, and regions. Results from the best model indicated that:(1) there were no substantial differences between bigeye tuna in the north or south Indian Ocean divided by 15°S; (2) individuals collected in the first and second quarters tended to gain more weight than those collected in the third and fourth quarters at the same longitude; and (3) individuals collected in 2015 and 2016 grew better, while those collected in 2014 and 2017 gained much less weight at the same fork length. The outcome from this study could assist the stock assessment and fishery management for this important tuna species in the Indian Ocean, and the methodology used in this paper can also be applied to the heterogeneity studies of other species in both coast water and far ocean.