SKU: 56366490065

Engine Oil Cooler for Mercedes-Benz C-Class W203/W204 & Mercedes-Benz CLC-Class - MAP-101114

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Description

Engine Oil Cooler for Mercedes-Benz C-Class W203/W204 & Mercedes-Benz CLC-Class - MAP-101114Vehicle Fitment & Part Details This engine oil cooler is listed for selected Mercedes Benz C Class W204 applications and cross references OE A2711880401. Supplied product specifications include aluminium material and 96 mm x 64 mm x 27 mm core dimensions. Confirm compatibility by matching the listed fitment details and any OEM reference numbers supplied. Verify VIN engine. Key Details SKU MAP 101114 Alternate Search Reference MAP101114 Component

Vehicle Fitment & Part Details

This engine oil cooler is listed for selected Mercedes-Benz C-Class W204 applications and cross-references OE A2711880401. Supplied product specifications include aluminium material and 96 mm x 64 mm x 27 mm core dimensions. Confirm compatibility by matching the listed fitment details and any OEM reference numbers supplied. Verify VIN/engine.

Key Details

SKU
MAP-101114
Alternate Search Reference
MAP101114
Component
Engine oil cooler
OEM References
  • Mercedes-Benz: 2711880401
  • Mercedes-Benz: A2711880401
Primary Fitment
Mercedes-Benz C-Class W204 (2007-2014)
Specs
Transmission type: manual / optional automatic transmission; material: aluminium; core length: 96 mm; core width: 64 mm; core depth: 27 mm

Listing Highlights

  • Listed for 21 supplied Mercedes-Benz applications shown in the fitment table.
  • Cross-referenced to Mercedes-Benz OE numbers 2711880401 and A2711880401 for compatibility checking before ordering.
  • Supplied specifications include aluminium material and 96 mm x 64 mm x 27 mm core dimensions.

Product Specifications

Specification Value
Transmission Type Manual / optional automatic transmission
Material Aluminium
Core Length 96 mm
Core Width 64 mm
Core Depth 27 mm

Vehicle Fitment

Includes 21 supplied applications covering selected Mercedes-Benz C-Class W203 and W204, C-Class Coupe CL203, C-Class T-Model S203 and S204, CLC-Class CL203, E-Class W211, E-Class T-Model S211 and SLK R171 models. Use the full table below to confirm model, chassis / platform, year range, engine, power and any other rendered columns before ordering.

View Full Vehicle Fitment (21 applications)
Model Chassis / Platform Years Engine Power
C-Class C 180 Kompressor (204.044, 204.045) W204 2007-2014 M 271.910 115 kW / 156 HP
C-Class C 180 Kompressor (203.046) W203 2000-2007 M 271.946 105 kW / 143 HP
C-Class C 200 Kompressor (203.042) W203 2000-2007 M 271.940 120 kW / 163 HP
C-Class C 180 Kompressor (204.046) W204 2007-2014 M 271.952 115 kW / 156 HP
C-Class C 200 Kompressor (204.041) W204 2007-2014 M 271.950 135 kW / 184 HP
C-Class Coupe C 180 Kompressor (203.746) CL203 2001-2008 M 271.946 105 kW / 143 HP
C-Class Coupe C 230 Kompressor (203.740) CL203 2001-2008 M 271.948 141 kW / 192 HP
C-Class T-Model C 180 Kompressor (203.246) S203 2001-2007 M 271.946 105 kW / 143 HP
C-Class T-Model C 200 Kompressor (203.242) S203 2001-2007 M 271.940 120 kW / 163 HP
C-Class T-Model C 180 Kompressor (204.245) S204 2007-2014 M 271.910 115 kW / 156 HP
C-Class T-Model C 180 Kompressor (204.246) S204 2007-2014 M 271.952 115 kW / 156 HP
C-Class T-Model C 200 Kompressor (204.241) S204 2007-2014 M 271.950 135 kW / 184 HP
CLC-Class CLC 160 (203.731) CL203 2008-2011 M 271.911 95 kW / 129 HP
CLC-Class CLC 180 Kompressor (203.746) CL203 2008-2011 M 271.946 105 kW / 143 HP
CLC-Class CLC 200 Kompressor (203.741) CL203 2008-2011 M 271.957 135 kW / 184 HP
E-Class E 200 Kompressor (211.041) W211 2002-2008 M 271.956 135 kW / 184 HP
E-Class E 200 Kompressor (211.042) W211 2002-2008 M 271.941 120 kW / 163 HP
E-Class T-Model E 200 Kompressor (211.241) S211 2003-2009 M 271.956 135 kW / 184 HP
E-Class T-Model E 200 T Kompressor (211.242) S211 2003-2009 M 271.941 120 kW / 163 HP
SLK 200 Kompressor (171.442) R171 2004-2011 M 271.944 120 kW / 163 HP
SLK 200 Kompressor (171.445) R171 2004-2011 M 271.954 135 kW / 184 HP

Fitment Notes

  • Always match OEM reference number(s) and vehicle details before ordering. Verify VIN/engine.

Compatibility Verification Notes

  • Confirm compatibility using VIN, engine code, chassis / platform, OE reference and original part comparison before ordering.
  • Match the supplied fitment details to your vehicle, including model, year range and any listed engine or chassis information.
  • Compare the supplied identifiers to the original part, including OE reference, core dimensions and transmission type where supplied.
  • Fitment data may vary by production date, market, import history and engine variant.
  • This listing is for part identification and compatibility checking only. Installation must be carried out according to the vehicle manufacturer's service information by a suitably qualified person.

Common Questions

Will this engine oil cooler fit my Mercedes-Benz C-Class?
This part (MAP-101114) is listed for the fitments shown on this page. Confirm by matching the fitment details. Verify VIN/engine.

What OEM part numbers does this cross-reference?
Cross-reference numbers are listed in the Key Details section above. Match at least one to your original part before ordering.

How do I confirm fitment if there are multiple variants?
Compare the supplied fitment details, OE reference numbers, original part details and any listed variant information before ordering. Verify VIN/engine.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 56366490065

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Amazon Customer
New York, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
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★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018
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Stergios Papadimitriou
Lexington, US
★★★★★ 5
The classic textbook on Deep Learning
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Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on May 23, 2026
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★★★★★ 5
This is the best purchase I’ve made in 2025!
Format: Paperback, Format: Paperback
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