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Here’s how many EVs were sold in the U.S. last year by model

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Tesla remained the top electric vehicle (EV) seller in the U.S. by a wide margin in 2024, gaining almost half of the emerging market and outselling the next several models combined with its own lineup, as shown in the latest data.

Cox Automotive released its Q4 and 2024 EV sales report last week, showing estimates of how many EVs were sold by brand and model, and highlighting how many units Tesla is delivering compared to other automakers for another year in a row. Total EV sales in the U.S. grew 7.3 percent year over year, amounting to a little over 1.3 million units—of which Tesla sold 633,762, or 48.7 percent.

Tesla’s total sales amounted to more than double those of the rest of the top 10 EVs sold in 2024, a list which was comprised of vehicles from General Motors (GM), Hyundai, Ford, and Rivian.

The Model Y and Model 3 were the top two EVs sold in 2024, with 372,613 and 189,903 units, respectively, as followed by the Ford Mustang Mach-E (51,745), the Hyundai Ioniq 5 (44,400), and the Cybertruck (38,965). By comparison, Tesla’s top three models outsold the rest of the top 10 EVs, which totaled 246,882, made up of the Ford F-150 Lightning, the Honda Prologue, the Chevy Equinox, the Cadillac Lyriq, and the Rivian R1S. The rest of the industry’s EVs combined made up 667,321 units, beating out Tesla’s total sales by just 33,559 units.

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READ MORE ON U.S. EV MARKET: Tesla dominated the top 10 best-selling EVs in the U.S. in 2023

You can see nearly all the EV models sold in the U.S. below, with the Tesla Model X and Model S landing in the 15th and 24th spots, respectively.

EV models sold in the U.S. in 2024

  1. Tesla Model Y: 372,613
  2. Tesla Model 3: 189,903
  3. Ford Mustang Mach-E: 51,745
  4. Hyundai Ioniq 5: 44,400
  5. Tesla Cybertruck: 38,965
  6. Ford F-150 Lightning: 33,510
  7. Honda Prologue: 33,017
  8. Chevy Equinox EV: 28,874
  9. Cadillac Lyriq: 28,402
  10. Rivian R1S: 26,934
  11. BMW i4: 23,403
  12. Chevy Blazer EV: 23,115
  13. Kia EV9: 22,017
  14. Kia EV6: 21,715
  15. Tesla Model X: 19,855
  16. Nissan Ariya: 19,798
  17. Toyota BZ4X: 18,570
  18. Volkswagen ID.4: 17,021
  19. BMW iX: 15,383
  20. GMC Hummer Truck/SUV: 13,993
  21. Rivian EDV500/700: 13,423
  22. Ford E-Transit: 12,610
  23. Subaru Solterra: 12,447
  24. Tesla Model S: 12,426
  25. Kia Niro: 12,367
  26. Hyundai Ioniq 6: 12,264
  27. Mercedes EQE: 11,660
  28. Audi Q4 e-tron: 11,356
  29. Nissan Leaf: 11,226
  30. Rivian R1T: 11,085
  31. Lexus RZ: 9,697
  32. Mercedes EQB: 8,885
  33. BMW i5: 8,763
  34. Chevy Bolt EV/EUV: 8,627
  35. Audi Q8 e-tron: 7,936
  36. Chevy Silverado EV: 7,428
  37. Acura ZDX: 7,391
  38. Mercedes EQS: 6,963
  39. Hyundai Kona EV: 5,063
  40. Porsche Taycan: 4,747
  41. BMW i7: 3,431
  42. Jaguar I-Pace: 3,304
  43. Mini Cooper: 3,118
  44. Volvo XC40: 2,995
  45. Genesis GV70: 2,976
  46. Audi e-tron: 2,894
  47. Genesis GV60: 2,866
  48. GMC Sierra EV: 1,788
  49. Porsche Macan: 1,739
  50. Brightdrop Zevo 600/400: 1,529
  51. Volvo C40: 1,420
  52. Volkswagen ID.Buzz: 1,162
  53. Audi Q6 e-tron: 966
  54. Fiat 500e: 929
  55. Volvo EX90: 749
  56. Cadillac Escalade EV: 670
  57. Mini Countryman: 549
  58. Mercedes G-Class: 455
  59. Genesis G80: 397
  60. Jeep Wagoneer: 231
  61. Volvo EX30: 229
  62. Mercedes E-Sprinter: 191

*Additional EV Models: 27,089

*At the time of writing, Cox has not yet responded to Teslarati‘s requests for comment on which models make up this figure, or on whether the figure includes Lucid, Polestar, or other brands that were omitted from the data.

Top 10 BEV sellers in the U.S. in 2024

  1. Tesla: 633,762
  2. GM: 112,897 (including Chevy, Cadillac and GMC)
  3. Ford: 97,865
  4. Hyundai: 61,727
  5. Kia: 56,099
  6. Rivian: 51,442
  7. Honda: 33,017
  8. Nissan: 31,024
  9. Mercedes-Benz: 28,154
  10. Audi: 23,152

You can see the full Cox Automotive spreadsheet on Q4 and 2024 U.S. EV sales here.

What are your thoughts? Let me know at zach@teslarati.com, find me on X at @zacharyvisconti, or send us tips at tips@teslarati.com.

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Zach is a renewable energy reporter who has been covering electric vehicles since 2020. He grew up in Fremont, California, and he currently lives in Colorado. His work has appeared in the Chicago Tribune, KRON4 San Francisco, FOX31 Denver, InsideEVs, CleanTechnica, and many other publications. When he isn't covering Tesla or other EV companies, you can find him writing and performing music, drinking a good cup of coffee, or hanging out with his cats, Banks and Freddie. Reach out at zach@teslarati.com, find him on X at @zacharyvisconti, or send us tips at tips@teslarati.com.

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Elon Musk

Tesla Optimus V3 hand and arm details revealed in new patents

Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.

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Credit: Tesla China

Tesla is planning to soon reveal its latest and greatest version of the Optimus humanoid robot, and a series of new patents for the hands and arms, with the former being, admittedly, one of the most challenging parts of developing the project.

Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.

The designs relocate heavy actuators to the forearm, route cables through a sophisticated wrist design, and employ innovative joint assemblies to achieve human-like dexterity while enabling lightweight construction and high-volume manufacturing.

Core Tendon-Driven Hand Architecture

The primary patent, which is titled “Mechanically Actuated Robotic Hand,” details a cable/tendon-driven system.

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Actuators are positioned in the forearm rather than the hand. Each finger features four degrees of freedom (DoF), while the wrist adds two more.

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Three thin, flexible control cables (tendons) per finger extend from the forearm actuators, pass through the wrist, and connect to the finger segments. Integrated channels within the finger phalanges guide these cables selectively—routing behind some joints and forward of others—to enable independent bending without unintended motion.

Patent diagrams illustrate thick cable bundles emerging from the wrist into the palm and fingers, with labeled pivots and routing guides. This setup closely mirrors human forearm-muscle and tendon anatomy, where most hand control originates proximally.

Advanced Wrist Routing Innovation

One of the standout features is the wrist’s cable transition mechanism. Cables shift from a lateral stack on the forearm side to a vertical stack on the hand side through a specialized transition zone.

This geometry significantly reduces cable stretch, torque, friction, and crosstalk during combined yaw and pitch wrist movements — common failure points in simpler tendon systems that cause imprecise or jerky motion.

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By minimizing these issues, the design supports smoother, more reliable multi-axis wrist operation, essential for complex real-world tasks.

Companion Patents on Appendage and Joint Design

Two supporting patents provide additional depth. “Robotic Appendage” covers the overall forearm-to-palm-to-finger assembly, with a palm body movably coupled to the forearm and finger phalanges linked by tensile cables returning to forearm actuators. Tensioning these cables repositions the phalanges precisely.

“Joint Assembly for Robotic Appendage” describes curved contact surfaces on mating structures paired with a composite flexible member. This allows smooth pivoting while maintaining consistent tension, enhancing durability, and simplifying assembly for mass production.

Executive Insights on Hand Development Challenges

Tesla executives have consistently described the hand as the most difficult component of Optimus.

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Elon Musk has called it “the majority of the engineering difficulty of the entire robot,” emphasizing that human hands possess roughly 27–28 DoF with an intricate tendon network powered largely by forearm muscles. He has likened the challenge to something “harder than Cybertruck or Model X… somewhere between Model X and Starship.”

Elon Musk shares ridiculous fact about Optimus’ hand demos

In mid-2025, Musk acknowledged that Tesla was “struggling” to finalize the hand and forearm design. By early 2026, he stated that the company had overcome the “hardest” problems, including human-level manual dexterity, real-world AI integration, and volume production scalability.

He estimated the electromechanical hand represents about 60 percent of the overall Optimus challenge, compounded by the lack of an existing supply chain for such precision components.

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These patents directly tackle the acknowledged pain points: relocating actuators reduces hand mass and inertia for better speed and efficiency; advanced wrist routing and joint geometry address friction and crosstalk; and simplified, stackable parts visible in the diagrams indicate readiness for high-volume manufacturing.

Implications for Optimus Production and Leadership

Collectively, the patents portray the Optimus v3 hand not as a mere prototype, but as a production-oriented system engineered from first principles.

The 22-DoF architecture, forearm-driven tendons, and crosstalk-minimizing wrist deliver a clear competitive edge in dexterity. They align with Musk’s view that high-volume manufacturing is one of the three critical elements missing from most other humanoid projects.

For Optimus to become the most capable humanoid robot, its hand needed to replicate the useful and applicable design of the human counterpart.

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These filings demonstrate that Tesla has transformed years of engineering challenges into patented, elegant solutions — positioning the company strongly in the race toward general-purpose robotics.

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Tesla intertwines FSD with in-house Insurance for attractive incentive

Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.

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tesla interior operating on full self driving
Credit: TESLARATI

Tesla intertwined its Full Self-Driving (Supervised) suite with its in-house Insurance initiative in an effort to offer an attractive incentive to drivers.

Tesla announced that its new Safety Score 3.0 will automatically have a perfect score of 100 with every mile driven with Full Self-Driving (Supervised) enabled.

The change is designed to boost customers’ average safety scores and deliver noticeably lower monthly premiums.

The move marks the clearest link yet between Tesla’s autonomous driving technology and its proprietary insurance product. Tesla Insurance already relies on real-time vehicle data—such as acceleration, braking, following distance, and speed—to calculate a Safety Score between 0 and 100. Higher scores have long translated into cheaper rates.

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Under the previous system, however, even brief manual interventions could drag down the average, frustrating owners who rely heavily on FSD. Version 3.0 eliminates that penalty for supervised autonomous miles, effectively treating FSD-driven segments as the safest possible driving behavior.

The incentive is immediate and financial. Drivers who keep FSD engaged for the majority of their trips will see their overall score rise, potentially shaving hundreds of dollars off annual premiums.

Tesla framed the update as a direct response to customer feedback, many of whom had complained that the old scoring model punished the very behavior it was meant to encourage.

For now, the program applies only to new policies in six states: Indiana, Tennessee, Texas, Arizona, Virginia, and Illinois.

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Existing policyholders are not yet included, a point that drew swift questions from the Tesla community. Many owners in other states, including California and Georgia, expressed hope that the benefit would expand nationwide soon.

The announcement arrives as Tesla continues to roll out FSD Supervised updates and push for regulatory approval of more advanced autonomy. By tying insurance savings directly to FSD usage, the company is putting its own actuarial weight behind the technology’s safety claims.

Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.

Tesla has not disclosed exact premium reductions or the full rollout timeline beyond the six launch states.

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Still, the message is clear: the more drivers trust FSD Supervised, the more Tesla Insurance will reward them. In an era when legacy insurers remain cautious about autonomous tech, Tesla is betting that its own data will prove the safest miles are the ones driven hands-free.

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Elon Musk

Tesla finalizes AI5 chip design, Elon Musk makes bold claim on capability

The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.

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Credit: Elon Musk | X

Tesla has finalized its chip design for AI5, as Elon Musk confirmed today that the new chip has reached the tape-out stage, the final step before mass production.

But in a brief reply on X, Musk clarified Tesla’s AI hardware roadmap, essentially confirming that the new chip will not be utilized for being “enough to achieve much better than human safety for FSD.”

He said that AI4 is enough to do that.

Instead, the AI5 chip will be focused on Tesla’s big-time projects for the future: Optimus and supercomputer clusters.

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Musk thanked TSMC and Samsung for production support, noting that AI5 could become “one of the most produced AI chips ever.” Yet, the key pivot came in his direct answer: vehicles no longer need the bleeding-edge silicon.

Existing AI4 hardware, which is already deployed in hundreds of thousands of HW4-equipped Teslas, delivers safety metrics superior to human drivers for Full Self-Driving. AI5 will instead accelerate Optimus robot development and massive Dojo-style training clusters.

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The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.

Now, with AI4 proving sufficient, the company avoids costly retrofits across its fleet while redirecting next-generation compute toward higher-value applications: dexterous robots and exponential training scale.

But is it reasonable to assume AI4 enables unsupervised self-driving? Yes, but with important caveats.

On the hardware side, the claim is credible. Tesla’s FSD stack runs end-to-end neural networks trained on billions of miles of real-world data. Internal safety data reportedly shows AI4-equipped vehicles already outperforming average human drivers by a significant margin in controlled metrics (collision avoidance, reaction time, edge-case handling).

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Dual-redundant AI4 chips provide ample headroom for the driving task, leaving bandwidth for future model improvements without new silicon. Musk’s assertion aligns with Tesla’s pattern of over-provisioning compute early, then optimizing ruthlessly, exactly as HW3 once sufficed before HW4 scaled further.

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Unsupervised autonomy, meaning Level 4 or higher, is not solely a compute problem. Regulatory approval remains the primary gate.

Even if AI4 achieves “much better than human” safety statistically, agencies like the NHTSA demand exhaustive validation, liability frameworks, and public trust.

Tesla’s supervised FSD has shown rapid gains in recent versions, yet real-world edge cases, like construction zones, emergency vehicles, and adverse weather, still require driver intervention in many jurisdictions. Competitors like Waymo operate limited unsupervised fleets, but only in geofenced areas with extensive mapping. Tesla’s vision-only, fleet-scale approach is more ambitious—and harder to certify globally.

In short, Musk’s post is both pragmatic and bullish. AI4 is likely capable of unsupervised FSD from a technical standpoint. Whether regulators and consumers agree, and how quickly, will determine if Tesla’s bet pays off.

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The company’s capital-efficient path keeps existing cars relevant while pouring future compute into robots. If the safety data holds, unsupervised autonomy could arrive sooner than many expect.

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